diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts
index d4c85f5e28..896abfcf70 100644
--- a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts
+++ b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts
@@ -6,14 +6,13 @@
  */
 
 import {Axis} from '@bokehjs/models/axes/axis';
-import {cumulativeSum} from '../stats/array';
+import {arrayMean, arrayMedian, cumulativeSum} from '../stats/array';
 import {scaleToOne} from '../stats/dataTransformation';
 import {
   interval as hdiInterval,
   data as hdiData,
 } from '../stats/highestDensityInterval';
 import {oneD} from '../stats/marginal';
-import {mean as computeMean} from '../stats/pointStatistic';
 import {interpolatePoints} from '../stats/utils';
 import * as interfaces from './interfaces';
 
@@ -46,6 +45,8 @@ export const updateAxisLabel = (axis: Axis, label: string | null): void => {
  * @param {number[]} marginalX - The support of the Kernel Density Estimate of the
  *     random variable.
  * @param {number[]} marginalY - The Kernel Density Estimate of the random variable.
+ * @param {number} activeStatistic - The statistic to show in the tool. 0 is the mean
+ *     and 1 is the median.
  * @param {number | null} [hdiProb=null] - The highest density interval probability
  *     value. If the default value is not overwritten, then the default HDI probability
  *     is 0.89. See Statistical Rethinking by McElreath for a description as to why this
@@ -62,6 +63,7 @@ export const computeStats = (
   rawData: number[],
   marginalX: number[],
   marginalY: number[],
+  activeStatistic: number,
   hdiProb: number | null = null,
   text_align: string[] = ['right', 'center', 'left'],
   x_offset: number[] = [-5, 0, 5],
@@ -72,17 +74,36 @@ export const computeStats = (
 
   // Compute the point statistics for the KDE, and create labels to display them in the
   // figures.
-  const mean = computeMean(rawData);
+  const mean = arrayMean(rawData);
+  const median = arrayMedian(rawData);
   const hdiBounds = hdiInterval(rawData, hdiProbability);
-  const x = [hdiBounds.lowerBound, mean, hdiBounds.upperBound];
-  const y = interpolatePoints({x: marginalX, y: marginalY, points: x});
-  const text = [
+  let x = [hdiBounds.lowerBound, mean, median, hdiBounds.upperBound];
+  let y = interpolatePoints({x: marginalX, y: marginalY, points: x});
+  let text = [
     `Lower HDI: ${hdiBounds.lowerBound.toFixed(3)}`,
     `Mean: ${mean.toFixed(3)}`,
+    `Median: ${median.toFixed(3)}`,
     `Upper HDI: ${hdiBounds.upperBound.toFixed(3)}`,
   ];
 
-  return {
+  // We will filter the output based on the active statistic from the tool.
+  let mask: number[] = [];
+  if (activeStatistic === 0) {
+    mask = [0, 1, 3];
+  } else if (activeStatistic === 1) {
+    mask = [0, 2, 3];
+  }
+  x = mask.map((i) => {
+    return x[i];
+  });
+  y = mask.map((i) => {
+    return y[i];
+  });
+  text = mask.map((i) => {
+    return text[i];
+  });
+
+  const output = {
     x: x,
     y: y,
     text: text,
@@ -90,6 +111,7 @@ export const computeStats = (
     x_offset: x_offset,
     y_offset: y_offset,
   };
+  return output;
 };
 
 /**
@@ -100,6 +122,8 @@ export const computeStats = (
  *     calculating the Kernel Density Estimate (KDE).
  * @param {number} hdiProbability - The highest density interval probability to use when
  *     calculating the HDI.
+ * @param {number} activeStatistic - The statistic to show in the tool. 0 is the mean
+ *     and 1 is the median.
  * @returns {interfaces.Data} The marginal distribution and cumulative
  *     distribution calculated from the given random variable data. Point statistics are
  *     also calculated.
@@ -108,6 +132,7 @@ export const computeData = (
   data: number[],
   bwFactor: number,
   hdiProbability: number,
+  activeStatistic: number,
 ): interfaces.Data => {
   const output = {} as interfaces.Data;
   for (let i = 0; i < figureNames.length; i += 1) {
@@ -125,7 +150,13 @@ export const computeData = (
     }
 
     // Compute the point statistics for the given data.
-    const stats = computeStats(data, distribution.x, distribution.y, hdiProbability);
+    const stats = computeStats(
+      data,
+      distribution.x,
+      distribution.y,
+      activeStatistic,
+      hdiProbability,
+    );
 
     output[figureName] = {
       distribution: distribution,
@@ -150,6 +181,7 @@ export const computeData = (
  *     application.
  * @param {interfaces.Figures} figures - Bokeh figures shown in the application.
  * @param {interfaces.Tooltips} tooltips - Bokeh tooltips shown on the glyphs.
+ * @param {interfaces.Widgets} widgets - Bokeh widget object for the tool.
  * @returns {number} We display the value of the bandwidth used for computing the Kernel
  *     Density Estimate in a div, and must return that value here in order to update the
  *     value displayed to the user.
@@ -162,29 +194,44 @@ export const update = (
   sources: interfaces.Sources,
   figures: interfaces.Figures,
   tooltips: interfaces.Tooltips,
+  widgets: interfaces.Widgets,
 ): number => {
-  const computedData = computeData(data, bwFactor, hdiProbability);
-  for (let i = 0; i < figureNames.length; i += 1) {
-    // Update all sources with new data calculated above.
-    const figureName = figureNames[i];
-    sources[figureName].distribution.data = {
-      x: computedData[figureName].distribution.x,
-      y: computedData[figureName].distribution.y,
-    };
-    sources[figureName].hdi.data = {
-      base: computedData[figureName].hdi.base,
-      lower: computedData[figureName].hdi.lower,
-      upper: computedData[figureName].hdi.upper,
-    };
-    sources[figureName].stats.data = computedData[figureName].stats;
-    sources[figureName].labels.data = computedData[figureName].labels;
+  const activeStatistic = widgets.stats_button.active as number;
+  const computedData = computeData(data, bwFactor, hdiProbability, activeStatistic);
 
-    // Update the axes labels.
-    updateAxisLabel(figures[figureName].below[0], rvName);
+  // Marginal figure.
+  // eslint-disable-next-line prefer-destructuring
+  const bandwidth = computedData.marginal.distribution.bandwidth;
+  sources.marginal.distribution.data = {
+    x: computedData.marginal.distribution.x,
+    y: computedData.marginal.distribution.y,
+  };
+  sources.marginal.hdi.data = {
+    base: computedData.marginal.hdi.base,
+    lower: computedData.marginal.hdi.lower,
+    upper: computedData.marginal.hdi.upper,
+  };
+  sources.marginal.stats.data = computedData.marginal.stats;
+  sources.marginal.labels.data = computedData.marginal.labels;
+  tooltips.marginal.distribution.tooltips = [[rvName, '@x']];
+  tooltips.marginal.stats.tooltips = [['', '@text']];
+  updateAxisLabel(figures.marginal.below[0] as Axis, rvName);
 
-    // Update the tooltips.
-    tooltips[figureName].stats.tooltips = [['', '@text']];
-    tooltips[figureName].distribution.tooltips = [[rvName, '@x']];
-  }
-  return computedData.marginal.distribution.bandwidth;
+  // Cumulative figure.
+  sources.cumulative.distribution.data = {
+    x: computedData.cumulative.distribution.x,
+    y: computedData.cumulative.distribution.y,
+  };
+  sources.cumulative.hdi.data = {
+    base: computedData.cumulative.hdi.base,
+    lower: computedData.cumulative.hdi.lower,
+    upper: computedData.cumulative.hdi.upper,
+  };
+  sources.cumulative.stats.data = computedData.cumulative.stats;
+  sources.cumulative.labels.data = computedData.cumulative.labels;
+  tooltips.cumulative.distribution.tooltips = [[rvName, '@x']];
+  tooltips.cumulative.stats.tooltips = [['', '@text']];
+  updateAxisLabel(figures.cumulative.below[0] as Axis, rvName);
+
+  return bandwidth;
 };
diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts
index fe8aede9e5..0011c98e38 100644
--- a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts
+++ b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts
@@ -8,6 +8,10 @@
 import {Plot} from '@bokehjs/models/plots/plot';
 import {ColumnDataSource} from '@bokehjs/models/sources/column_data_source';
 import {HoverTool} from '@bokehjs/models/tools/inspectors/hover_tool';
+import {Div} from '@bokehjs/models/widgets/div';
+import {RadioButtonGroup} from '@bokehjs/models/widgets/radio_button_group';
+import {Select} from '@bokehjs/models/widgets/selectbox';
+import {Slider} from '@bokehjs/models/widgets/slider';
 
 // NOTE: In the corresponding Python typing files for the diagnostic tool, we define
 //       similar types using a TypedDict object. TypeScript allows us to maintain
@@ -95,3 +99,11 @@ export interface Tooltips {
   marginal: Tooltip;
   cumulative: Tooltip;
 }
+
+export interface Widgets {
+  rv_select: Select;
+  bw_factor_slider: Slider;
+  bw_div: Div;
+  hdi_slider: Slider;
+  stats_button: RadioButtonGroup;
+}
diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/array.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/array.ts
index 39dcf65bd0..5e37e8aed9 100644
--- a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/array.ts
+++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/array.ts
@@ -5,6 +5,29 @@
  * LICENSE file in the root directory of this source tree.
  */
 
+/**
+ * Syntactic sugar for summing an array of numbers.
+ *
+ * @param {number[]} data - The array of data.
+ * @returns {number} The sum of the array of data.
+ */
+export const arraySum = (data: number[]): number => {
+  return data.reduce((previousValue, currentValue) => {
+    return previousValue + currentValue;
+  });
+};
+
+/**
+ * Calculate the mean of the given array of data.
+ *
+ * @param {number[]} data - The array of data.
+ * @returns {number} The mean of the given data.
+ */
+export const arrayMean = (data: number[]): number => {
+  const dataSum = arraySum(data);
+  return dataSum / data.length;
+};
+
 /**
  * Cumulative sum of the given data.
  *
@@ -128,3 +151,23 @@ export const valueCounts = (data: number[]): {[key: string]: number} => {
   }
   return counts;
 };
+
+/**
+ * Calculate the median value for the given array.
+ *
+ * @param {number[]} data - Numerical array of data.
+ * @returns {number} The median value of the given data.
+ */
+export const arrayMedian = (data: number[]): number => {
+  const sortedArray = numericalSort(data);
+  const arrayLength = sortedArray.length;
+  const isEven = sortedArray.length % 2 === 0;
+  let median;
+  if (isEven) {
+    const index = arrayLength / 2;
+    median = (sortedArray[index - 1] + sortedArray[index]) / 2;
+  } else {
+    median = sortedArray[Math.floor(arrayLength / 2)];
+  }
+  return median;
+};
diff --git a/src/beanmachine/ppl/diagnostics/tools/marginal1d/tool.py b/src/beanmachine/ppl/diagnostics/tools/marginal1d/tool.py
index 1e122cba87..fc8129aa08 100644
--- a/src/beanmachine/ppl/diagnostics/tools/marginal1d/tool.py
+++ b/src/beanmachine/ppl/diagnostics/tools/marginal1d/tool.py
@@ -24,14 +24,14 @@ class Marginal1d(DiagnosticToolBaseClass):
 
     Attributes:
         data (Dict[str, List[List[float]]]): JSON serializable representation of the
-            given `mcs` object.
+            given ``mcs`` object.
         rv_names (List[str]): The list of random variables string names for the given
             model.
         num_chains (int): The number of chains of the model.
         num_draws (int): The number of draws of the model for each chain.
         palette (List[str]): A list of color values used for the glyphs in the figures.
-            The colors are specifically chosen from the Colorblind palette defined in
-            Bokeh.
+            The colors are specifically chosen from the ``Colorblind`` palette defined
+            in Bokeh.
         tool_js (str):The JavaScript callbacks needed to render the Bokeh tool
             independently from a Python server.
     """
@@ -40,6 +40,12 @@ def __init__(self: Marginal1d, mcs: MonteCarloSamples) -> None:
         super(Marginal1d, self).__init__(mcs)
 
     def create_document(self: Marginal1d) -> Model:
+        """
+        Create the Bokeh document for the diagnostic tool.
+
+        Returns:
+            Model: A Bokeh Model object.
+        """
         # Initialize widget values using Python.
         rv_name = self.rv_names[0]
         bw_factor = 1.0
@@ -110,6 +116,7 @@ def create_document(self: Marginal1d) -> Model:
                 sources,
                 figures,
                 tooltips,
+                widgets,
               );
             }} catch (error) {{
               {self.tool_js}
@@ -121,6 +128,7 @@ def create_document(self: Marginal1d) -> Model:
                 sources,
                 figures,
                 tooltips,
+                widgets,
               );
             }}
         """
@@ -135,6 +143,7 @@ def create_document(self: Marginal1d) -> Model:
             "figures": figures,
             "tooltips": tooltips,
             "toolView": tool_view,
+            "widgets": widgets,
         }
 
         # Each widget requires slightly different JS, except for the sliders.
@@ -155,10 +164,12 @@ def create_document(self: Marginal1d) -> Model:
         """
         rv_select_callback = CustomJS(args=callback_arguments, code=rv_select_js)
         slider_callback = CustomJS(args=callback_arguments, code=slider_js)
+        button_callback = CustomJS(args=callback_arguments, code=slider_js)
 
         # Tell Python to use the JavaScript.
         widgets["rv_select"].js_on_change("value", rv_select_callback)
         widgets["bw_factor_slider"].js_on_change("value", slider_callback)
         widgets["hdi_slider"].js_on_change("value", slider_callback)
+        widgets["stats_button"].js_on_change("active", button_callback)
 
         return tool_view
diff --git a/src/beanmachine/ppl/diagnostics/tools/marginal1d/typing.py b/src/beanmachine/ppl/diagnostics/tools/marginal1d/typing.py
index 8796cc70e3..a826f519ef 100644
--- a/src/beanmachine/ppl/diagnostics/tools/marginal1d/typing.py
+++ b/src/beanmachine/ppl/diagnostics/tools/marginal1d/typing.py
@@ -12,6 +12,7 @@
 from bokeh.models.glyphs import Circle, Line
 from bokeh.models.sources import ColumnDataSource
 from bokeh.models.tools import HoverTool
+from bokeh.models.widgets.groups import RadioButtonGroup
 from bokeh.models.widgets.inputs import Select
 from bokeh.models.widgets.markups import Div
 from bokeh.models.widgets.sliders import Slider
@@ -20,15 +21,16 @@
 
 # NOTE: These are the types pyre gives us when using `reveal_type(...)` on the outputs
 #       of the methods.
-StatsAndLabelsData = Dict[str, Dict[str, Any]]
-HDIData = Dict[str, Any]
-Data = Dict[Any, Any]
+Data = Dict[
+    str,
+    Dict[str, Union[Dict[str, List[Any]], Dict[str, Union[List[Any], float]]]],
+]
 Sources = Dict[Any, Any]
 Figures = Dict[Any, Any]
 Glyphs = Dict[Any, Any]
 Annotations = Dict[Any, Any]
 Tooltips = Dict[Any, Any]
-Widgets = Dict[str, Union[Div, Select, Slider]]
+Widgets = Dict[str, Union[Div, RadioButtonGroup, Select, Slider]]
 
 
 # NOTE: TypedDict objects are for reference only. Due to the way pyre accesses keys in
@@ -72,7 +74,64 @@ class _GlyphData(TypedDict):  # pyre-ignore
     labels: _LabelsData
 
 
-class _Data(TypedDict):  # pyre-ignore
+class Data_(TypedDict):  # pyre-ignore
+    """
+    Definition of the Marginal 1D tool's data.
+
+    Attributes:
+        marginal (Dict[str, Dict[str, Union[List[int], List[float], List[str], float]]]):
+            Data for the marginal figure.
+        cumulative (Dict[str, Dict[str, Union[List[int], List[float], List[str], float]]]):
+            Data for the cumulative figure.
+
+    .. code-block:: python
+
+      {
+          "marginal": {
+              "distribution": {
+                  "x": list[float],
+                  "y": list[float],
+                  "bandwidth": float,
+              },
+              "hdi": {
+                  "base": list[float],
+                  "lower": list[float],
+                  "upper": list[float],
+              },
+              "stats": {"x": list[float], "y": list[float], "text": list[str]},
+              "labels": {
+                  "x": list[float],
+                  "y": list[float],
+                  "text": list[str],
+                  "text_align": list[str],
+                  "x_offset": list[int],
+                  "y_offset": list[int],
+              },
+          },
+          "cumulative": {
+              "distribution": {
+                  "x": list[float],
+                  "y": list[float],
+                  "bandwidth": float,
+              },
+              "hdi": {
+                  "base": list[float],
+                  "lower": list[float],
+                  "upper": list[float],
+              },
+              "stats": {"x": list[float], "y": list[float], "text": list[str]},
+              "labels": {
+                  "x": list[float],
+                  "y": list[float],
+                  "text": list[str],
+                  "text_align": list[str],
+                  "x_offset": list[int],
+                  "y_offset": list[int],
+              },
+          },
+      }
+    """
+
     marginal: _GlyphData
     cumulative: _GlyphData
 
@@ -84,12 +143,80 @@ class _Source(TypedDict):  # pyre-ignore
     labels: ColumnDataSource
 
 
-class _Sources(TypedDict):  # pyre-ignore
+class Sources_(TypedDict):  # pyre-ignore
+    """
+    Definition of the Marginal 1D tool's Bokeh ``ColumnDataSource`` objects.
+
+    Attributes:
+        marginal (Dict[str, ColumnDataSource]): Bokeh ``ColumnDataSource`` objects for
+            the marginal figure.
+        cumulative (Dict[str, ColumnDataSource]): Bokeh ``ColumnDataSource`` objects for
+            the cumulative figure.
+
+    .. code-block:: python
+
+       {
+           "marginal": {
+               "distribution": ColumnDataSource({"x": list[float], "y": list[float]}),
+               "hdi": ColumnDataSource(
+                   {"base": list[float], "lower": list[float], "upper": list[float]}
+               ),
+               "stats": ColumnDataSource(
+                   {"x": list[float], "y": list[float], "text": list[str]}
+               ),
+               "labels": ColumnDataSource(
+                   {
+                       "x": list[float],
+                       "y": list[float],
+                       "text": list[str],
+                       "text_align": list[str],
+                       "x_offset": list[int],
+                       "y_offset": list[int],
+                   }
+               ),
+           },
+           "cumulative": {
+               "distribution": ColumnDataSource({"x": list[float], "y": list[float]}),
+               "hdi": ColumnDataSource(
+                   {"base": list[float], "lower": list[float], "upper": list[float]}
+               ),
+               "stats": ColumnDataSource(
+                   {"x": list[float], "y": list[float], "text": list[str]}
+               ),
+               "labels": ColumnDataSource(
+                   {
+                       "x": list[float],
+                       "y": list[float],
+                       "text": list[str],
+                       "text_align": list[str],
+                       "x_offset": list[int],
+                       "y_offset": list[int],
+                   }
+               ),
+           },
+       }
+    """
+
     marginal: _Source
     cumulative: _Source
 
 
-class _Figures(TypedDict):  # pyre-ignore
+class Figures_(TypedDict):  # pyre-ignore
+    """
+    Definition of the Marginal 1D tool's figures.
+
+    Attributes:
+        marginal (Figure): Bokeh ``Figure`` object for the marginal.
+        cumulative (Figure): Bokeh ``Figure`` object for the cumulative.
+
+    .. code-block:: python
+
+       {
+           "marginal": Figure,
+           "cumulative": Figure,
+       }
+    """
+
     marginal: Figure
     cumulative: Figure
 
@@ -109,7 +236,33 @@ class _FigureGlyphs(TypedDict):  # pyre-ignore
     stats: _StatsGlyph
 
 
-class _Glyphs(TypedDict):  # pyre-ignore
+class Glyphs_(TypedDict):  # pyre-ignore
+    """
+    Definition of the Marginal 1D tool's glyphs.
+
+    These are different than the tool's annotations, which are defined in an annotation
+    object.
+
+    Attributes:
+        marginal (Dict[str, Dict[str, Union[Circle, Line]]]): Bokeh ``Glyph`` objects
+            for the marginal figure.
+        cumulative (Dict[str, Dict[str, Union[Circle, Line]]]): Bokeh ``Glyph`` objects
+            for the cumulative figure.
+
+    .. code-block:: python
+
+       {
+           "marginal": {
+               "distribution": {"glyph": Line, "hover_glyph": Line},
+               "stats": {"glyph": Circle, "hover_glyph": Circle},
+           },
+           "cumulative": {
+               "distribution": {"glyph": Line, "hover_glyph": Line},
+               "stats": {"glyph": Circle, "hover_glyph": Circle},
+           },
+       }
+    """
+
     marginal: _FigureGlyphs
     cumulative: _FigureGlyphs
 
@@ -119,7 +272,26 @@ class _FigureAnnotations(TypedDict):  # pyre-ignore
     labels: LabelSet
 
 
-class _Annotations(TypedDict):  # pyre-ignore
+class Annotations_(TypedDict):  # pyre-ignore
+    """
+    Definition of the Marginal 1D tool's annotations.
+
+    These are different than the tool's glyphs, which are defined in an glyph object.
+
+    Attributes:
+        marginal (Dict[str, Union[Band, LabelSet]]): Bokeh ``Annotation`` objects for
+            the marginal figure.
+        cumulative (Dict[str, Union[Band, LabelSet]]): Bokeh ``Annotation`` objects for
+            the cumulative figure.
+
+    .. code-block:: python
+
+       {
+           "marginal": {"hdi": Band, "labels": LabelSet},
+           "cumulative": {"hdi": Band, "labels": LabelSet},
+       }
+    """
+
     marginal: _FigureAnnotations
     cumulative: _FigureAnnotations
 
@@ -129,13 +301,57 @@ class _Tooltip(TypedDict):  # pyre-ignore
     stats: HoverTool
 
 
-class _Tooltips(TypedDict):  # pyre-ignore
+class Tooltips_(TypedDict):  # pyre-ignore
+    """
+    Definition of the Marginal 1D tool's tooltips.
+
+    Attributes:
+        marginal (Dict[str, HoverTool]): Bokeh ``HoverTool`` objects defining the
+            marginal figure's hover tools.
+        cumulative (Dict[str, HoverTool]): Bokeh ``HoverTool`` objects defining the
+            cumulative figure's hover tools.
+
+    .. code-block:: python
+
+       {
+           "marginal": {"distribution": HoverTool, "stats": HoverTool},
+           "cumulative": {"distribution": HoverTool, "stats": HoverTool},
+       }
+    """
+
     marginal: _Tooltip
     cumulative: _Tooltip
 
 
-class _Widgets(TypedDict):  # pyre-ignore
+class Widgets_(TypedDict):  # pyre-ignore
+    """
+    Definition of the Marginal 1D tool's widgets.
+
+    Attributes:
+        rv_select (Select): Bokeh ``Select`` widget that contains the random variable
+            names.
+        bw_factor_slider (Slider): Bokeh ``Slider`` widget that allows the user to
+            change the bandwidth used to calculate the Kernel Density Estimate (KDE).
+        bw_div (Div): Bokeh ``Div`` widget that displays the bandwidth used when
+            calculating the KDE.
+        hdi_slider (Slider): Bokeh ``Slider`` widget that adjusts the Highest Density
+            Interval highlighted on the tool.
+        stats_button (RadioButtonGroup): Bokeh ``RadioButtonGroup`` that allows the user
+            to visualize the point statistic shown by the button.
+
+    .. code-block:: python
+
+       {
+           "rv_select": Select,
+           "bw_factor_slider": Slider,
+           "bw_div": Div,
+           "hdi_slider": Slider,
+           "stats_button": RadioButtonGroup,
+       }
+    """
+
     rv_select: Select
     bw_factor_slider: Slider
     bw_div: Div
     hdi_slider: Slider
+    stats_button: RadioButtonGroup
diff --git a/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py b/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py
index ff148175c3..03eefaadc8 100644
--- a/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py
+++ b/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py
@@ -14,6 +14,7 @@
 from bokeh.models.layouts import Column, Row
 from bokeh.models.sources import ColumnDataSource
 from bokeh.models.tools import HoverTool
+from bokeh.models.widgets.groups import RadioButtonGroup
 from bokeh.models.widgets.inputs import Select
 from bokeh.models.widgets.markups import Div
 from bokeh.models.widgets.panels import Panel, Tabs
@@ -62,12 +63,11 @@
 
 
 def create_sources() -> typing.Sources:
-    """Create Bokeh sources from the given data that will be bound to glyphs.
+    """
+    Create Bokeh sources that will be bound to glyphs.
 
-    Returns
-    -------
-    typing.Sources
-        A dictionary of Bokeh ColumnDataSource objects.
+    Returns:
+        typing.Sources: A dictionary of Bokeh ColumnDataSource objects.
     """
     output = {}
     for figure_name, figure_data in EMPTY_DATA.items():
@@ -80,17 +80,14 @@ def create_sources() -> typing.Sources:
 
 
 def create_figures(rv_name: str) -> typing.Figures:
-    """Create the Bokeh figures used for the tool.
+    """
+    Create the Bokeh figures used for the tool.
 
-    Parameters
-    ----------
-    rv_name : str
-        The string representation of the random variable data.
+    Args:
+        rv_name (str): The string representation of the random variable data.
 
-    Returns
-    -------
-    typing.Figures
-        A dictionary of Bokeh Figure objects.
+    Returns:
+        typing.Figures: A dictionary of Bokeh Figure objects.
     """
     output = {}
     for figure_name in FIGURE_NAMES:
@@ -112,12 +109,11 @@ def create_figures(rv_name: str) -> typing.Figures:
 
 
 def create_glyphs() -> typing.Glyphs:
-    """Create the glyphs used for the figures of the tool.
+    """
+    Create the glyphs used for the figures of the tool.
 
-    Returns
-    -------
-    typing.Glyphs
-        A dictionary of Bokeh Glyphs objects.
+    Returns:
+        typing.Glyphs: A dictionary of Bokeh Glyphs objects.
     """
     palette = plotting_utils.choose_palette(num_colors=2)
     output = {}
@@ -173,21 +169,16 @@ def add_glyphs(
     glyphs: typing.Glyphs,
     sources: typing.Sources,
 ) -> None:
-    """Bind source data to glyphs and add the glyphs to the given figures.
-
-    Parameters
-    ----------
-    figures : typing.Figures
-        A dictionary of Bokeh Figure objects.
-    glyphs : typing.Glyphs
-        A dictionary of Bokeh Glyphs objects.
-    sources : typing.Sources
-        A dictionary of Bokeh ColumnDataSource objects.
-
-    Returns
-    -------
-    None
-        Adds data bound glyphs to the given figures directly.
+    """
+    Bind source data to glyphs and add the glyphs to the given figures.
+
+    Args:
+        figures (typing.Figures): A dictionary of Bokeh Figure objects.
+        glyphs (typing.Glyphs): A dictionary of Bokeh Glyphs objects.
+        sources (typing.Sources): A dictionary of Bokeh ColumnDataSource objects.
+
+    Returns:
+        None: Adds data bound glyphs to the given figures directly.
     """
     for figure_name, figure_glyphs in glyphs.items():
         fig = figures[figure_name]
@@ -203,17 +194,14 @@ def add_glyphs(
 
 
 def create_annotations(sources: typing.Sources) -> typing.Annotations:
-    """Create any annotations for the figures of the tool.
+    """
+    Create any annotations for the figures of the tool.
 
-    Parameters
-    ----------
-    source : typing.Sources
-        A dictionary of Bokeh ColumnDataSource objects.
+    Args:
+        source (typing.Sources): A dictionary of Bokeh ColumnDataSource objects.
 
-    Returns
-    -------
-    typing.Annotations
-        A dictionary of Bokeh Annotation objects.
+    Returns:
+        typing.Annotations: A dictionary of Bokeh Annotation objects.
     """
     palette = plotting_utils.choose_palette(num_colors=1)
     output = {}
@@ -250,19 +238,15 @@ def create_annotations(sources: typing.Sources) -> typing.Annotations:
 
 
 def add_annotations(figures: typing.Figures, annotations: typing.Annotations) -> None:
-    """Add the given annotations to the given figures of the tool.
-
-    Parameters
-    ----------
-    figures : typing.Figures
-        A dictionary of Bokeh Figure objects.
-    annotations : typing.Annotations
-        A dictionary of Bokeh Annotation objects.
-
-    Returns
-    -------
-    None
-        Adds annotations directly to the given figures.
+    """
+    Add the given annotations to the given figures of the tool.
+
+    Args:
+        figures (typing.Figures): A dictionary of Bokeh Figure objects.
+        annotations (typing.Annotations): A dictionary of Bokeh Annotation objects.
+
+    Returns:
+        None: Adds annotations directly to the given figures.
     """
     for figure_name, annotation_sources in annotations.items():
         fig = figures[figure_name]
@@ -271,19 +255,15 @@ def add_annotations(figures: typing.Figures, annotations: typing.Annotations) ->
 
 
 def create_tooltips(rv_name: str, figures: typing.Figures) -> typing.Tooltips:
-    """Create hover tools for the glyphs used in the figures of the tool.
-
-    Parameters
-    ----------
-    rv_name : str
-        The string representation of the random variable data.
-    figures : typing.Figures
-        A dictionary of Bokeh Figure objects.
-
-    Returns
-    -------
-    typing.Tooltips
-        A dictionary of Bokeh HoverTools objects.
+    """
+    Create hover tools for the glyphs used in the figures of the tool.
+
+    Args:
+        rv_name (str): The string representation of the random variable data.
+        figures (typing.Figures): A dictionary of Bokeh Figure objects.
+
+    Returns:
+        typing.Tooltips: A dictionary of Bokeh HoverTools objects.
     """
     output = {}
     for figure_name, fig in figures.items():
@@ -311,19 +291,15 @@ def create_tooltips(rv_name: str, figures: typing.Figures) -> typing.Tooltips:
 
 
 def add_tooltips(figures: typing.Figures, tooltips: typing.Tooltips) -> None:
-    """Add the given tools to the figures.
-
-    Parameters
-    ----------
-    figures : typing.Figures
-        A dictionary of Bokeh Figure objects.
-    tooltips : typing.Tooltips
-        A dictionary of Bokeh HoverTools objects.
-
-    Returns
-    -------
-    None
-        Adds the tooltips directly to the given figures.
+    """
+    Add the given tools to the figures.
+
+    Args:
+        figures (typing.Figures): A dictionary of Bokeh Figure objects.
+        tooltips (typing.Tooltips): A dictionary of Bokeh HoverTools objects.
+
+    Returns:
+        None: Adds the tooltips directly to the given figures.
     """
     for figure_name, figure_tooltips in tooltips.items():
         fig = figures[figure_name]
@@ -337,25 +313,20 @@ def create_widgets(
     bw_factor: float,
     bandwidth: float,
 ) -> typing.Widgets:
-    """Create the widgets used in the tool.
-
-    Parameters
-    ----------
-    rv_name : str
-        The string representation of the random variable data.
-    rv_names : List[str]
-        A list of all available random variable names.
-    bw_factor : float
-        Multiplicative factor used when calculating the kernel density estimate.
-    bandwidth : float
-        The bandwidth used to calculate the KDE.
-
-    Returns
-    -------
-    typing.Widgets
-        A dictionary of Bokeh widget objects.
     """
-    return {
+    Create the widgets used in the tool.
+
+    Args:
+        rv_name (str): The string representation of the random variable data.
+        rv_names (List[str]): A list of all available random variable names.
+        bw_factor (float): Multiplicative factor used when calculating the kernel
+            density estimate.
+        bandwidth (float): The bandwidth used to calculate the KDE.
+
+    Returns:
+        typing.Widgets: A dictionary of Bokeh widget objects.
+    """
+    output = {
         "rv_select": Select(value=rv_name, options=rv_names, title="Query"),
         "bw_factor_slider": Slider(
             title="Bandwidth factor",
@@ -366,16 +337,17 @@ def create_widgets(
         ),
         "bw_div": Div(text=f"Bandwidth: {bw_factor * bandwidth}"),
         "hdi_slider": Slider(start=1, end=99, step=1, value=89, title="HDI"),
+        "stats_button": RadioButtonGroup(labels=["Mean", "Median"], active=0),
     }
+    return output
 
 
 def help_page() -> Div:
-    """Help tab for the tool.
+    """
+    Help tab for the tool.
 
-    Returns
-    -------
-    Div
-        Bokeh Div widget containing the help tab information.
+    Returns:
+        Div: Bokeh Div widget containing the help tab information.
     """
     text = """
     <h2>
@@ -413,45 +385,44 @@ def help_page() -> Div:
     return Div(text=text, disable_math=False, min_width=PLOT_WIDTH)
 
 
-def create_figure_grid(figures: typing.Figures) -> Row:
-    """Layout the given figures in a grid, and make one toolbar.
-
-    Parameters
-    ----------
-    figures : typing.Figures
-        A dictionary of Bokeh Figure objects.
-
-    Returns
-    -------
-    Row
-        A Bokeh layout object.
+def create_view(widgets: typing.Widgets, figures: typing.Figures) -> Tabs:
     """
-    toolbar = plotting_utils.create_toolbar(figures=list(figures.values()))
-    return Row(children=[*list(figures.values()), toolbar], css_classes=["bk-loading"])
+    Create the tool view.
 
+    Args:
+        widgets (typing.Widgets): A dictionary of Bokeh widget objects.
+        figures (typing.Figures): A dictionary of Bokeh Figure objects.
 
-def create_view(widgets: typing.Widgets, figures: typing.Figures) -> Tabs:
-    """Create the tool view.
-
-    Parameters
-    ----------
-    widgets : typing.Widgets
-        A dictionary of Bokeh widget objects.
-    figures : typing.Figures
-        A dictionary of Bokeh Figure objects.
-
-    Returns
-    -------
-    Tabs
-        Bokeh Tabs objects.
+    Returns:
+        Tabs: Bokeh Tabs objects.
     """
+    toolbar = plotting_utils.create_toolbar(figures=list(figures.values()))
     help_panel = Panel(child=help_page(), title="Help", name="helpPanel")
     fig_child = Column(
         children=[
-            create_figure_grid(figures),
-            widgets["bw_factor_slider"],
-            widgets["bw_div"],
-            widgets["hdi_slider"],
+            widgets["stats_button"],
+            Column(
+                children=[
+                    Row(
+                        children=[
+                            Column(
+                                children=[
+                                    figures["marginal"],
+                                    widgets["bw_factor_slider"],
+                                ]
+                            ),
+                            Column(
+                                children=[
+                                    figures["cumulative"],
+                                    widgets["hdi_slider"],
+                                ]
+                            ),
+                            toolbar,
+                        ]
+                    ),
+                    widgets["bw_div"],
+                ],
+            ),
         ],
         css_classes=["bm-tool-loading", "arcs"],
     )
diff --git a/tutorials/Coin_flipping.ipynb b/tutorials/Coin_flipping.ipynb
index 1b6dfcba52..49b9d265fc 100644
--- a/tutorials/Coin_flipping.ipynb
+++ b/tutorials/Coin_flipping.ipynb
@@ -193,7 +193,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
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\n",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -442,7 +442,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -534,7 +534,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7e6d2ba3dc26408cbc6068ac8f5fdd74",
+       "model_id": "682a369270f344f884a8bf91dc0abc52",
        "version_major": 2,
        "version_minor": 0
       },
@@ -548,7 +548,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "850b7048b4794eb9aada0cc613bbd4b7",
+       "model_id": "1cc5f1a03d844697ad0e470a7ec8946c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -562,7 +562,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c98a004ddd04406a87ff21b1f6285b7f",
+       "model_id": "d3d9a04987a44b5e964ad7e7f58f3bc7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -576,7 +576,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "33158a68ef394eb3a13a9a81b5a5a57e",
+       "model_id": "c5342707dc9b4672ad78b9470a2600cd",
        "version_major": 2,
        "version_minor": 0
       },
@@ -662,7 +662,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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AAJgagRe5Ur9+/WSxWHTs2LEsvc5isahFixZuqSk9gYGBCgwMzFTbmTNnymKxaObMmW6tyRWOHTsmi8Wifv365XQpAADcFoEXkv4XYG71qF27dk6XCWRJdv8wupekpaVp6tSpqlmzpvLnz69ixYqpS5cuiomJyfK2du7cqc6dO6tChQrKnz+/ypcvryeffFIbN2502b63b9+uxx9/XIULF5a3t7eCgoI0d+7cLNcKAK7kmdMF4N5SsWJF9erVK911JUuWvMvVZGzs2LF64403VLp06Sy97rffflOBAgXcVBXgegMHDtT06dP14IMPatCgQfrzzz+1YMECrV69Wlu2bNGDDz6Yqe0sWbJEzzzzjKxWq5566imVLVtWJ06c0LfffqulS5cqMjLSacQ+q/uOiopSSEiIvLy81K1bN/n5+Wnx4sXq2bOnjh07pjfffNNV3QIAWULghYNKlSopPDw8p8u4rVKlSqlUqVJZfl21atXcUA3gHuvXr9f06dPVrFkzrVmzRlarVZLUp08ftWnTRi+++KI2bNiQqW29+eabMgxDW7Zscfi2ZufOnapfv77GjBnjEHizuu/U1FSFhobKYrFo48aNqlOnjiQpLCxMDz/8sMLCwtS5c2dVrlz5DnsFALKOKQ3INtt82NjYWPXo0UP+/v4qWLCg2rVrpyNHjkiSDh48qKeeekpFihRRwYIF1blzZ509e9ZhOzfOB923b58ee+wx+fn5ydfXVx06dNCvv/7qtO/0vqqOioqSxWJReHi4tm7dqpCQEBUqVEgWi8Wp5ptdvXpVkydPVlBQkAoWLCgfHx89+OCDGjp0qOLi4uzt1q9fr2effVZVq1aVj4+PfHx8VL9+ff3nP/+5w9509OOPP6pp06by9vZW0aJF1bdvX124cCHdtnv37lW3bt1UqlQpeXl5qXz58ho0aFC67b/44gs9+eSTCgwMVL58+VSkSBGFhIRo/fr16W772rVrGj9+vCpVqqR8+fKpUqVKGjt2rNLS0tJtHxMTo/79+6tChQrKly+f/P39VbduXf3zn//M1HHbfq5HjhzRxIkTVb16dVmtVnsQO3XqlMLCwtSoUSMVL15cVqtVgYGBeumll5zeV4GBgZo1a5YkqUKFCvapOTf//I8eParQ0FCVK1dOVqtVpUqVUr9+/XT8+PFM1exO06dPlyS9++679sApSa1bt1ZISIg2btyo33//PVPbOnLkiAICApymJtWtW1elSpVy6r+s7nvdunU6fPiwevToYQ+7klSwYEG99dZbSk1NVWRkZOYOHABcjBFe3JG4uDg1bdpUJUuWVN++ffX7779r2bJlOnDggJYuXapmzZqpbt26evbZZ/Xzzz9r4cKFunjxotasWeO0rSNHjqhJkyYKCgrSSy+9pJiYGH377bfavHmztmzZon/84x+ZqmnLli16//331bJlS73wwgv6448/btk+KSnJ/gu8cuXK6t+/v6xWq2JiYjRt2jT16dNHhQsXliSNHz9ehw4dUqNGjfTUU0/p4sWLWrlypQYMGKCDBw/qo48+ynon3uT777/XsmXL1KFDB7344ovauHGjvvzySx0+fFibN292aLt06VJ16dJFHh4eeuKJJ1S2bFn9+uuvmjp1qlatWqX//ve/9tol6eWXX1atWrX0yCOPqFixYoqNjdWSJUv0yCOPaPHixXryyScdtv/CCy/oiy++UIUKFfTyyy8rKSlJEyZM0JYtW5zqPnXqlIKCgpSYmKh27dqpa9euunz5smJiYjRlypQs9c2gQYO0bds2tWvXTu3bt1eJEiUkSRs3btRHH32k1q1bq2HDhsqbN6927dqlTz/9VKtWrdLOnTvl5+cnSRo8eLBmzpypPXv26LXXXlOhQoUkyeEkwv/+978KCQlRYmKiOnTooEqVKunYsWOaM2eOVqxYoa1bt+qBBx7IdN2uFhUVJW9vbzVp0sRpXUhIiFauXKkNGzaoSpUqt91W9erVtXv3bu3du1c1a9a0L9+9e7dOnz6t9u3b39G+o6KiJEmPPvqoU3vbssyORgOAq2Up8MbGxuqbb77RDz/8oAMHDujMmTMqUqSImjRpouHDh6thw4ZOr0lISFB4eLgWLVqkM2fOqGTJknrmmWcUHh4uX1/fdPczd+5cTZo0Sfv375eXl5cefvhhjRkzRvXr18/eUd6BZCNZ56+dv+v7zQ5/D39ZLdbbN7yFQ4cOZTiloVGjRmrbtq3Dsr1792rIkCGaMGGCfdmLL76oadOmqWnTpgoPD9drr70mSTIMQ+3bt9cPP/ygXbt2OYwCSdKmTZs0atQovfPOO/ZlX375pfr27atXXnlFP/74Y6aOYc2aNfr888/17LPPZqr922+/rY0bN6p3796KjIyUh4eHfV18fLzD808//VQVKlRweH1qaqoef/xxTZ48Wa+99prKlSuXqf1mZOnSpYqKirIHjWvXrumRRx5RVFSUtm3bpkaNGkmSLly4oN69e6tYsWKKjo522O+8efPUo0cPvf3225oyZYp9+a+//upU/+nTp1W/fn0NGzbMIfBGRUXpiy++UK1atRQdHS1vb29J178aT+8ExkWLFunixYuaPHmyXn31VYd1589n7d/Q3r17tWvXLqe+bNWqlc6cOSMfHx+H5bb3ydSpUzVy5EhJ1wPv7t27tWfPHg0ePNjpahkpKSnq1q2b0tLStGPHDtWqVcu+bvPmzWrRooVee+01ff/997etd8mSJdq9e3emj69Fixa3vVpIYmKiTp8+rYceesjhPWhjmxqQ2ZPXJkyYoPbt26tx48Z66qmnVKZMGZ08eVKLFy9Ws2bN9Nlnn93Rvm3/n96UhcKFC8vf3z9bJ9oBgCtkKfBOmTJF48ePV8WKFdWmTRsVL15cMTExWrJkiZYsWaJ58+apS5cu9vaJiYkKDg7W7t271aZNG3Xv3l179uzRxIkTtX79em3evNn+S9Tm/fff18iRI1WuXDkNHDhQly9f1vz589WkSROtWrXqrl5SSpLOXzuvhZcW3tV9Zlengp1U2jNrJ3Hd7PDhwxo9enS661577TWnwOvj4+MQUCWpR48emjZtmooWLeoQfCwWi7p166YffvhBe/bscQq8hQsX1htvvOGwrHfv3vrggw+0bt06nThxQmXLlr3tMdSpUyfTYffatWv67LPP5Ofnp8mTJzv9creNFtrcHBYlydPTUwMHDtSaNWu0fv169e3bN1P7zkiPHj0cRtU8PDzUt29fRUVFafv27fbA++WXXyohIUH//ve/nYJh9+7d9eGHH2r+/PkOgTe9+kuVKqVnnnlGU6ZM0fHjx1W+fHn79qXrfxDc+O+0dOnSeu211/TWW2+lW3/+/Pmdlvn7+2f28CVJw4YNS/cPh+LFi6fbvnfv3ho0aJDWrl1rD7y3s2zZMh07dkzvvPOOQ9iVpKZNm+rJJ5/UkiVLlJCQkOEf5zZLliyxT5/IrNt9lsXHx0tyfg/a2Gqytbud4OBgbdy4UZ07d9bs2bPty8uWLav+/fs7zInPzr4z85qTJ09mqlYAcLUsBd6goCBt3LhRzZo1c1i+adMmtW7dWi+++KKefPJJ+3yviIgI7d69W8OHD9f48ePt7cPCwjRmzBhFREQ4hKuYmBiFhYWpSpUq+umnn+wfnK+++qqCgoIUGhqqAwcOyNOTmRjuYvuqMrMqV67s9EeL7RdnzZo1HebP3rguNjbWaVt16tRx2pbFYlHTpk21b98+7dmzJ1OBNygoKNP1HzhwQAkJCXrkkUccvvrPyKVLl/Thhx9qyZIlOnz4sBITEx3Wnzp1KtP7zkjdunWdlpUpU0aSdPHiRfuybdu22f976NAhp9ckJSXp/PnzOn/+vD1wHjlyRGPHjtW6desUGxur5ORkp/ptgXfPnj2S5PTvPaNl7du31xtvvKGXX35Za9asUdu2bdW0adNMfd1+s1v9DBcvXqzPPvtMO3fuVFxcnK5du+ZQf2bZ+u/AgQPpfqtx5swZpaWl6ffff7/tt0szZ86856+fvGLFCnXr1k3t27fX999/r8DAQB0/flzvv/+++vfvr7179zp8UwP3C3xjucu2FVD6irrecIGdzp9u1alY5z8+76Zj49rl6P6BG2UpOT799NPpLm/WrJlatmyp1atX65dfflH9+vVlGIZmzJghHx8fvf322w7tR4wYoSlTpujzzz9XeHi4PRRFRkYqNTVVI0eOdBglqF69uvr06aNp06Zp3bp16c4RQ85Ib+TL9gfJrdalpKQ4rcto9M42fzOzI1m29plhC5CZubzZ1atX1aJFC+3cuVN16tRR7969VbRoUXl6eurYsWOaNWuWU4DMjvRGyGz9dmO4++uvvyRJ//73v2+5vcTERPn7++vQoUMKCgpSQkKCWrZsqQ4dOsjX11d58uRRVFSUNmzY4FB/fHy88uTJk+7obHp9XKFCBW3dulWjR4/WihUr9M0330iSqlatqnfeeUedO3fOxNFnvH1J+uijj/Svf/1LxYoV06OPPqoyZcrYR5QnTZqUpf639d+cOXNu2e7mP2ruFtv7IKP3fUJCgkO7W/nrr7/Uo0cPVa5cWV999ZXy5Ll+vnK1atU0a9YsxcTEaPLkyXr55ZdVsWLFbO07M6/JTK0A4A4uGyrNmzfv9Q3+/y/mmJgYnTp1SiEhIU6jdvny5VPz5s313Xff6dChQ/Y5X7c66SEkJETTpk3Thg0b7mrg9ffwV6eCne7a/u6Ev0fWvja+19x8lrjNn3/+KSlzv9glOY0q34rtRKb0Rpxv9t1332nnzp0KDQ21n8FuM3/+/Cx/pX2nbH9Q/PLLL3rooYdu237ixImKi4vT7Nmz1bNnT4d1AwcOdDqhyM/PT2lpaTp//ryKFSvmsM72M7lZzZo1tWjRIqWkpOjnn3/WihUr9PHHH6tr164KCAhI9wSo9KT3M0xNTdU777yjgIAA7d6926EmwzAUERGRqW3b2Prv+++/dzphK6vcMYfX29tbpUqV0tGjR3Xt2jWn6Ta3mjN7s+joaF28eFHBwcH2sGtjsVjUsmVLbdu2Tbt27VLFihWzte8b5/XWq1fPoX1cXJzOnz+vxo0b37ZWAHAHlwTeP/74Q2vXrlXJkiVVo0YNSbf/ML7xw/HG//fx8Un3BgeZPUEjOTnZYZTHNhKRXVaL9Y7nxSJzdu3apcTERKc/kKKjoyXJaZ6lK1StWlW+vr7avn274uLibjmt4fDhw5KkJ554wmndpk2bXF7b7TRs2FCLFy/W1q1bMxV4M6o/LS3N3sc3qlWrlnbu3KlNmzY5fbtzu+PNmzevGjVqpEaNGqlSpUrq06ePli1blunAm57z588rPj5erVu3dgrgO3bs0JUrV5xeYwtqN46M29hOst26datLAq+r5/BK1+fdzp8/X9HR0WrevLnDulWrVtnb3M7Vq1clSefOnUt3vW35jZcfy+q+g4ODNXbsWK1evVrdunVzaL969epM1woA7nDH1+FNSUlR7969lZycrIiICPsvmOye9HCnJ2iMHTtWfn5+9kdm5nzi3hAXF6dx48Y5LPvyyy/1yy+/qFWrVm75WXp6emrAgAGKj4/Xa6+95hSM4uPjdfnyZUmyz229+dJgGzZscBrxvRv69++vggULauTIkdq/f7/T+r///ts+T1XKuP7x48dr3759Tq/v06ePJGnMmDEOX+vHxsZq8uTJTu23b9+e7ii9bTQ4vZPZsqJ48eLKnz+/du7cqb///tu+PC4uToMGDUr3NUWKFJGkdE+WevLJJ1WuXDlNmDAh3VvrpqSkOPVVRmbOnCnDMDL9yOzNXV544QVJ0qhRo+yhVbp+neZVq1apefPmTnOkDx8+rAMHDjhMG2rUqJE8PDy0cOFC7d2716H9/v37NW/ePFmtVj388MPZ3nfr1q31wAMPaO7cuQ6j3ZcuXdI777wjT09Ppzu5AcDdckcjvGlpaXr22We1ceNGPf/88+rdu7er6sq2ESNGaOjQofbnCQkJhN4suNVlySS59S5szZo108cff6xt27apQYMG+v333/Xtt9/Kz89PU6dOddt+x4wZo23btumrr77Stm3b9Nhjj8lqterIkSNauXKlNm/erNq1a6tDhw4KDAxURESE9u3bp4ceekgHDx7UsmXL1LFjRy1atMhtNaanWLFimjdvnjp37qxatWqpbdu2qlatmpKSknT8+HFt2LBBjRs3tp+EOHDgQEVGRurpp59W165dVbRoUW3btk07d+5Uu3bttHy54wk0LVq0UP/+/RUZGakaNWroqaeeUnJyshYsWKBGjRpp2bJlDu3nzJmjTz75RC1atFClSpXk6+urX3/9VT/88IP8/f0zfeWMjOTJk0cvvfSSPvroI9WqVUsdOnRQQkKCVqxYofLlyysgIMDpNa1atdKHH36oAQMGqHPnzvL29la5cuXUo0cPWa1WLVy4UI899piCg4PVunVr+0j5H3/8oU2bNqlo0aI6cODAHdV9J1q2bKnQ0FDNmDFDderUUbt27ey39/X19dWnn37q9JrWrVvr+PHjOnr0qP1SbKVLl9aIESP07rvvqkGDBurYsaMCAwP1xx9/6Ntvv1VycrI++OADh/naWd23p6enZsyYoZCQEDVr1kzdu3eXr6+vFi9erKNHj+rdd9/N1gmMAOAK2Q68hmHo+eef1+zZs9WrVy9NmzbNYX12T3q40xM0rFarw9dyyJpbXZZMcm/gfeCBB/Tvf/9bw4cP19SpU2UYhh577DGNHz8+0zedyI58+fJpzZo1mjp1qmbPnq3p06fLw8PDfmk8W2jw8fHRunXrNGzYMG3cuFFRUVGqXr265syZoxIlStz1wCtJ7dq1065du/TBBx9o7dq1WrNmjby9vVWmTBn1799fvXr977TtOnXqaPXq1Ro1apQWL14sDw8PNW7cWNHR0Vq6dKlT4JWu322rSpUqmj59uqZOnaoyZcpo6NCh6tKli1Pg7d69u5KSkhQdHa3t27crOTlZZcqU0csvv6x//etf9itN3ImxY8eqSJEimjlzpj755BOVKFFC3bp10+jRo9Od1vHYY48pIiJC06dP1/jx45WSkqLg4GD16NFDktSgQQPt2bNHH3zwgX744Qdt3rxZVqtVpUuXVseOHdW9e/c7rvlOffbZZ6pZs6Y+++wzffzxx/Lx8VGHDh303nvvZSlAvvPOO/btrFmzxn4SWXBwsAYNGpTutI6s7rtly5bavHmzwsLC9PXXX+vq1auqXr263nnnHad54wBwN1kMwzCy+qK0tDSFhoYqMjJS3bt311dffeV0UsPvv/+uqlWrZniZq44dO+q7777T77//bp+f27hxY23dulWnT592mse7ZMkSPfXUU3rzzTf13nvvZbpW24d6fHz8La+lmZSUpKNHj9pviYq759ixY6pQoYL69u17z1/aCYB72T6LQxcfV+yl/00xqlDcW4PaVbU/n7L8oI6edd8VNOqf3K+Fc163P+/Uc7x2lKnutv3dqeuXJfvfybcLZpfmsmTIVTKb17Iry3N4bwy7Xbt2TTfsStdPMgsICFB0dLTTZX2SkpK0ceNGBQQEqFKlSvblthMabCc43CgrJ2gAAAAANlkKvGlpaXruuecUGRlpv1tPemFXun6pm9DQUF2+fFljxoxxWDd27FjFxcUpNDTU4fJD/fv3l6enp9577z2HqQ379+/Xl19+qYoVK6pVq1ZZKRkAAAD3uSzN4R0zZoxmzpwpHx8fValSRe+++65Tm44dO6p27dqSpOHDh2vp0qWKiIjQrl27VK9ePe3Zs0crVqxQ7dq1NXz4cIfXVqlSReHh4Ro1apRq1qypTp06KTExUfPmzVNKSoqmT5/OXdYAAACQJVlKj8eOHZMkXb58OcN5tIGBgfbA6+3traioKI0ePVoLFy5UVFSUSpYsqSFDhigsLMzpequSNHLkSAUGBmrSpEn69NNP5eXlpcaNG2vMmDFq0KBB1o4OuUJgYKCyMZUcAAAgU7IUeLNzv3g/Pz9NmDAhS/do79mzJ2f0AgAAwCXu+MYTAAAAwL2MwAsAAABTI/ACAADA1Ai8AAAAMDUCLwAAAEyNwAsAAABTI/ACAADA1Ai8AAAAMDUCL+5bM2fOlMViyfLNVAAAQO5C4IWk67eNtlgsDg8vLy+VLVtWPXr00N69e91eQ1RUlCwWi8LDw92+L5jTqlWr1KJFC/n6+qpgwYJq0aKFVq1alaVtBAYGOv1buPmxadMmh9dMmTJF/fv3V82aNeXp6SmLxaKoqKh0t5/ev7WbHx4eHtntAgBAOrJ0a2GYX8WKFdWrVy9J0uXLl7Vt2zbNmzdPixcv1rp169S4ceMcrtB1nnrqKTVq1EilSpXK6VLgAnPmzFGvXr3k7++vvn37ymKx6Ouvv1bbtm01e/bsTN+ufPDgwbp48aLT8vPnz+vf//63ChcurAYNGjise/XVVyVJpUqVUrFixXTmzJkMt1+oUCGFhYWlu27Hjh1avny5QkJCMlUrACBzCLxwUKlSJacR1lGjRum9997TyJEjtX79+pwpzA38/Pzk5+eX02XABeLi4vTKK6/I399fO3fuVNmyZSVJI0aMUN26dfXKK6/o8ccfV+HChW+7rcGDB6e7/KOPPpIk9erVS/ny5XNYt2zZMtWrV08lS5bUwIED9dlnn2W4/UKFCmX4LUaHDh0kSaGhobetEwCQeUxpwG0NGjRIkrR9+3b7stTUVE2cOFG1atVS/vz55efnp5YtW2r58uVOr09LS9OMGTMUFBSkIkWKqECBAgoMDFTHjh21ceNGSVJ4eLhatmwpSRo9erTD17vHjh2zb+vq1auaMGGC6tatK29vbxUsWFDNmjXT0qVLnfbbr18/WSwWHTlyRBMnTlT16tVltVrVr18/Sbeew7tlyxa1a9dORYoUUb58+VStWjWFh4fr77//dmprsVjUokULxcbGql+/fipZsqTy5MmT4VfaNi1atJDFYlFycrLefPNNlStXTvnz51e9evW0du1aSdKlS5f06quvqnTp0sqXL58efvhh7dixI93tnT17VkOGDFGlSpVktVrl7++vZ555Rvv27XNqu379ej377LOqWrWqfHx85OPjo/r16+s///lPutu2HeO5c+f07LPPqnjx4sqfP78aNWp02+O8G7755htdvHhRgwYNsodd6fqIq23E9ptvvrmjfXz++eeSpOeee85pXbt27VSyZMk72v6pU6e0YsUKFS9e3B58AQCuwQjvbaRcS1N8cmpOl5EpflZP5fVw/d8wFovF4blhGOratasWL16sKlWq6OWXX1ZiYqK+/vprtW/fXpMnT7Z/xStdH2WLiIhQxYoV1aNHDxUsWFCxsbHatGmT1q1bp+bNm6tFixY6duyYZs2apeDgYLVo0cL++kKFCkmSkpOT1bZtW0VFRalOnTp67rnnlJKSouXLl+vJJ5/UlClT9MorrzjVP2jQIG3btk3t2rVT+/btVaJEiVse76JFi9StWzd5eXmpa9euKl68uNauXavRo0dr9erVWr9+vaxWq8NrLly4oIcfflhFihRR165ddfXqVfn6+maqf7t27apffvlFTzzxhK5cuaI5c+aoffv22rJliwYMGKCkpCR16tRJ586d04IFCxQSEqKjR486bP/w4cP20P3oo4+qY8eOOnv2rBYtWqRVq1bpxx9/VMOGDe3tx48fr0OHDqlRo0Z66qmndPHiRa1cuVIDBgzQwYMH7aOZN7p48aKaNGkiX19f9ezZU2fPnrXX8/PPP+uhhx7K1PG6gy10P/roo07rQkJC9MYbb2jDhg164YUXsrX9LVu26LffflP9+vVVq1atOyk1QzNnztS1a9fUp08f5c2b1y37AID7FYH3NuKTU7XxxIWcLiNTmpctKv8CXi7f7scffyxJ9nmLs2fP1uLFixUcHKzVq1fLy+v6PkeOHKl69erpX//6lzp06KAKFSpIkmbMmKHSpUtr7969KlCggH27hmEoLi5OkuwBd9asWWrRokW6X/mOGTNGUVFRCg8P19tvv20P4pcuXVKrVq30z3/+U08//bQCAgIcXrd3717t2rVL5cqVu+2xXrp0SaGhofLw8NDWrVtVs2ZNe629evXS3Llz9cEHH2jUqFEOr9u3b5/69++v6dOnZ/mEo/Pnz2vv3r3y9vaWdD20devWTa1bt1abNm00d+5ceXpe/6dau3Ztvf766/r88881ZMgQ+zb69OmjM2fOaNWqVWrTpo19+ahRo1S/fn09//zzDicefvrpp/afj01qaqoef/xxTZ48Wa+99ppTf+3Zs0cvvfSSpkyZojx5rv9h1apVK4WGhmrq1KmaNm3abY919+7dWrJkSab7JjAw0D4ifysxMTGSpMqVKzutsy2ztckO2+iuu6YaGIahL774QlL6I8gAgDtD4IWDQ4cO2cOm7aS16Oho5cuXT++//74k2acARERE2MOuJJUpU0ZDhgzRiBEjNGfOHIdQ6OXlZQ9tNhaLRUWKFMlUXWlpafr0009VqVIlh7ArSQULFtTbb7+tJ554QosXL3Ya5R02bFimwq4kLVmyRBcvXtSLL75oD7u2WseNG6evv/5aM2fOdAq8Xl5eioiIyNbZ9e+995497EpSp06dlDdvXl28eFEffvihQ791795dr7/+uvbs2WNftmvXLm3ZskXPPfecQ9iVpCpVquj555/XhAkTtG/fPvso7M1hV5I8PT01cOBArVmzRuvXr1ffvn0d1nt7e2v8+PH2sCtJffv21cCBAx2mu9zK7t27NXr06Ey1laTg4OBMBd74+HhJSndOtre3tzw8POxtsury5cv6+uuvVaBAAXXv3j1b27idDRs26PDhw2ratKmqVavmln0AwP2MwAsHhw8ftgeSvHnzqkSJEurRo4feeOMN1ahRQ9L1gJU/f34FBQU5vd42Urt79277si5dumjatGl66KGH1LVrVwUHB+vhhx92CHm3c/DgQcXFxSkgICDdwHTu3DlJ0oEDB5zWpVdnRnbt2uVwHDcqW7asKlasqIMHD+rSpUsqWLCgfV2FChXk7++f6f3cqE6dOg7PPTw8VLx4cSUmJjoFddsVJWJjY+3Ltm3bJkk6c+ZMuiPjtj45cOCAPfBeunRJH374oZYsWaLDhw8rMTHR4TWnTp1y2k7lypXl4+PjsMzT01MlSpRI96oG6enXr1+mAuy9ZMGCBbp8+bL69u2b6WkqWXWr+cEAgDtH4L0NP6unmpctmtNlZIqf9c5/nCEhIVq5cuUt2yQkJDicGHQj24k7N46mffzxx3rggQc0c+ZMvfvuu3r33XeVL18+denSRR999FGmguJff/0lSdq/f7/279+fYbubg5uk287ZvVFCQsItX1OyZEkdPHhQCQkJDoE3K/u4WXohytPTM93RSttob0pKin2ZrW+WL1+e7kmDNra+uXr1qlq0aKGdO3eqTp066t27t4oWLSpPT0/7POrk5GSn12d0RQtPT09du3btFkfofrba4uPjVbSo47/XxMREXbt2LdtX5JgxY4Yk901nuHjxohYtWiRfX1916dLFLfsAgPsdgfc28nrkccu82NzM19dXf/75Z7rrbMtvDHF58+bVsGHDNGzYMJ06dUobNmxQZGSkvvzyS/u808zsU5KeeeYZLVy4MEv13nzSXWb2k5Xjy+o+XM1WS0Yn7d3su+++086dOxUaGqrp06c7rJs/f75mzZrlljol983hrVy5snbs2KGYmBinwHur+b238+uvv2rbtm2qVq2amjZtmuXXZ8bcuXN15coV9enTx2GOOwDAdQi8yLI6depo3bp1+umnn5ymC2zYsEHS9ZOr0hMQEKDu3bura9euqlatmtauXasrV64of/789vmv6Y0W/uMf/5Cvr6927NihlJQUt53FbpteEBUV5TTaFhsbq8OHD+uBBx5wGN3NabarL2zdujVTgffw4cOSpCeeeMJp3c13EHM1d83hDQ4O1rx587R69Wo1atTIYZ3tD6rg4OAs1SrdnakG7j4hDgDAdXiRDbaTmUaMGOHw1XpsbKwmTJggT09P+12tkpOTtW7dOhmG4bCNxMREXbp0SXnz5rUHXdsJbCdPnnTap6enp1588UUdP35c//rXvxz2a7Nv3z6dPXv2jo7tySeflJ+fnyIjIx2mThiGYT/ee20OalBQkBo2bKh58+ZpwYIFTuvT0tLsf4hIUvny5SVJmzdvdmi3YcMGpxFfV+vXr58Mw8j0I7PX+O3SpYv8/Pw0ZcoUnThxwr789OnTmjRpkgoVKqTOnTs7vOb06dM6cOBAhiezpaSk6KuvvlLevHnVp0+fbB/zrezevVs7d+5UzZo1Vb9+fbfsAwDACC+yoXfv3lq8eLG+++471axZU+3bt7dfh/fChQv66KOP9MADD0iSrly5otatW+uBBx5Qw4YNVa5cOV2+fFnLli3TmTNn9Prrr9uv9FCtWjUFBARo/vz5KlCggMqUKSOLxaIXX3xRfn5+Gj16tHbu3KmPP/5Yy5cvV3BwsIoVK6bY2Fj98ssv2rNnj7Zu3arixYtn+9h8fX01ffp0de/eXQ0bNlTXrl1VrFgx/fjjj9qxY4eCgoI0bNgwl/SjK82bN08tW7ZUt27dNGnSJNWrV0/58uXTH3/8oa1bt+rcuXNKSkqSdP1uXoGBgYqIiLBfueHgwYNatmyZOnbsqEWLFuXw0WRd4cKFNXXqVPXu3Vt169ZVt27dlCdPHi1YsEB//vmnvvrqK6e7rI0YMUKzZs1SZGRkun/ELF26VOfOndPTTz992/fUuHHj7CcHbt261b7MdkWT0NDQdKdEMLoLAHcHgRdZZrFYtHDhQk2ePFmzZs3SlClT5OXlpbp162ro0KEOX5XbLmX1448/atOmTTp79qwKFy6satWqafz48eratau9rYeHhxYvXqzXX39dX331lS5duiRJ6tatm/z8/GS1WrVixQp9/vnn+vLLL7Vw4UIlJyerRIkSevDBBzVw4ED7lSTuROfOnVWyZEmNHTtWixcv1t9//63AwEC99dZbev31151uK3svqFChgnbt2qUJEyZoyZIl+uKLL+Th4aFSpUqpefPm6tSpk72tj4+P1q1bp2HDhmnjxo2KiopS9erVNWfOHJUoUSJXBl7p+i1//f39NXbsWHvQrFu3rmbNmqWQkJAsby8rYXTlypUOo+iSHOamt2jRwinwJiUlac6cObJarerVq1eW6wMAZJ7FuPm7ZpNJSEiQn5+f4uPjb3lJoaSkJB09elQVKlS4JwMNANwPbJ/FoYuPK/bS/+bzVyjurUHtqtqfT1l+UEfPOl+VxVXqn9yvhXNetz/v1HO8dpSp7rb93amA0lfUtdf/Lle4YHZpnYrNn4MVScfGtcvR/SN3yWxeyy7m8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQLvTUx+lTYAuKfZPoPT+CgG4EIE3v/n6Xn9Hhypqak5XAkA3L9stw1PTCHxAnAdAu//8/DwkIeHhxISEnK6FAC4LxmGofj4eFmtViUkp+V0OQBMhFsL/z+LxaLixYvr9OnTslqt8vb2lsViyemyAMD0DMNQSkqK4uPjdfnyZZUuXTqnSwJgMgTeG/j5+enKlSs6f/68zp07l9PlAMB9xWq1qnTp0m65rSiA+xuB9wYWi0WlSpVS8eLF7fPIAADu5+Hhobx58+Z0GQBMisCbDtt8XgAAAOR+nLQGAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQ8c7oAAEDWBb6xPKdLAIBcgxFeAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpeeZ0AQDgaoFvLM/pEgAA9xBGeAEAAGBqBF4AAACYWpYD7+zZszVgwADVr19fVqtVFotFM2fOTLdteHi4LBZLuo98+fJluI+5c+cqKChI3t7eKly4sB5//HHt2LEjq6UCAAAAWZ/DO2rUKB0/flz+/v4qVaqUjh8/ftvX9O3bV4GBgY479kx/1++//75GjhypcuXKaeDAgbp8+bLmz5+vJk2aaNWqVWrRokVWSwYAAMB9LMuBd8aMGapcubLKly+vcePGacSIEbd9Tb9+/TIVVGNiYhQWFqYqVarop59+kp+fnyTp1VdfVVBQkEJDQ3XgwIEMwzIAAABwsyxPaXjkkUdUvnx5d9SiyMhIpaamauTIkfawK0nVq1dXnz59dPjwYa1bt84t+wYAAIA53ZWT1jZt2qSIiAh99NFHWr58uZKTk9NtFxUVJUl69NFHndaFhIRIkjZs2OC2OgEAAGA+d2VuwNtvv+3wvFSpUpo1a5batGnjsDwmJkY+Pj4qWbKk0zYqV65sb3MrycnJDoE6ISEhu2UDAADABNw6wlu7dm3NmjVLx44d05UrVxQTE6N33nlHFy9e1BNPPKE9e/Y4tI+Pj3eYynAjX19fe5tbGTt2rPz8/OyPsmXLuuZgAAAAkCu5NfB27NhRffr0Ufny5ZUvXz5VqlRJo0aN0uTJk5WUlKR3333X5fscMWKE4uPj7Y8TJ064fB8AAADIPXLkxhN9+/aVp6enoqOjHZb7+fllOIJrm5qQ0QiwjdVqla+vr8MDAAAA968cCbxeXl4qWLCg/v77b4fllStX1uXLl3XmzBmn19jm7trm8gIAAACZkSOBNyYmRnFxcU43owgODpYkrV692uk1q1atcmgDAAAAZIbbAu+lS5e0d+9ep+VxcXF67rnnJEndu3d3WNe/f395enrqvffec5jasH//fn355ZeqWLGiWrVq5a6SAQAAYELZutPa5s2bJUm//PKLfZntGrodO3ZUx44ddeHCBdWqVUv169dXjRo1VLx4ccXGxmrFihW6cOGC2rRpoyFDhjhsu0qVKgoPD9eoUaNUs2ZNderUSYmJiZo3b55SUlI0ffp07rIGAACALMlyety8ebNmzZrlsCw6Otp+AlpgYKA6duyoIkWK6OWXX9a2bdv0/fff6+LFi/L29laNGjXUq1cvhYaGysPDw2n7I0eOVGBgoCZNmqRPP/1UXl5eaty4scaMGaMGDRpk8zABAABwv8py4J05c6Zmzpx523a+vr6aOnVqdmpSz5491bNnz2y9FgAAALhRjpy0BgAAANwtBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYWpYD7+zZszVgwADVr19fVqtVFotFM2fOzLB9QkKChg4dqvLly8tqtap8+fIaOnSoEhISMnzN3LlzFRQUJG9vbxUuXFiPP/64duzYkdVSAQAAgKwH3lGjRuk///mPjh8/rlKlSt2ybWJiooKDgzVx4kRVrVpVQ4YM0YMPPqiJEycqODhYiYmJTq95//331bNnT/35558aOHCgunTpoujoaDVp0kRRUVFZLRcAAAD3uSwH3hkzZujYsWM6d+6cBg4ceMu2ERER2r17t4YPH67Vq1dr3LhxWrFihd5++23t3r1bERERDu1jYmIUFhamKlWqaO/evfroo4/02WefacuWLfL09FRoaKhSU1OzWjIAAADuY1kOvI888ojKly9/23aGYWjGjBny8fHR22+/7bBuxIgRKly4sD7//HMZhmFfHhkZqdTUVI0cOVJ+fn725dWrV1efPn10+PBhrVu3LqslAwAA4D7mtpPWYmJidOrUKTVp0kTe3t4O6/Lly6fmzZsrNjZWhw4dsi+3TVl49NFHnbYXEhIiSdqwYcMt95ucnKyEhASHBwAAAO5fnu7acExMjCSpcuXK6a63LY+JiXH4fx8fH5UsWfKW7W9l7NixGj16dLbrBgAAdy7wjeU5XYJbHRvXLqdLQBa4bYQ3Pj5ekhymJtzI19fXoZ3t/7PSPj0jRoxQfHy8/XHixIks1w4AAADzcNsIb06xWq2yWq05XQYAAADuEW4b4bWN1GY0ImubW3vjiK6fn1+W2gMAAAC347bAe7s5t+nN8a1cubIuX76sM2fOZKo9AAAAcDtuDbwBAQGKjo52usFEUlKSNm7cqICAAFWqVMm+PDg4WJK0evVqp+2tWrXKoQ0AAACQGW4LvBaLRaGhobp8+bLGjBnjsG7s2LGKi4tTaGioLBaLfXn//v3l6emp9957z2Fqw/79+/Xll1+qYsWKatWqlbtKBgAAgAll+aS1GTNmaPPmzZKkX375xb7Mdg3djh07qmPHjpKk4cOHa+nSpYqIiNCuXbtUr1497dmzRytWrFDt2rU1fPhwh21XqVJF4eHhGjVqlGrWrKlOnTopMTFR8+bNU0pKiqZPny5PT9OdZwcAAAA3ynJ63Lx5s2bNmuWwLDo6WtHR0ZKkwMBAe+D19vZWVFSURo8erYULFyoqKkolS5bUkCFDFBYW5nRDCkkaOXKkAgMDNWnSJH366afy8vJS48aNNWbMGDVo0CAbhwgAAID7WZYD78yZMzVz5sxMt/fz89OECRM0YcKETL+mZ8+e6tmzZ1ZLAwAAAJy4bQ4vAAAAcC8g8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFPzzOkCANx9gW8sz+kSAAC4axjhBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApnZXAm9gYKAsFku6j4EDBzq1T0hI0NChQ1W+fHlZrVaVL19eQ4cOVUJCwt0oFwAAACbiebd25Ofnp8GDBzstr1+/vsPzxMREBQcHa/fu3WrTpo26d++uPXv2aOLEiVq/fr02b94sb2/vu1Q1AAAAcru7FngLFSqk8PDw27aLiIjQ7t27NXz4cI0fP96+PCwsTGPGjFFERIRGjx7txkoBAABgJvfUHF7DMDRjxgz5+Pjo7bffdlg3YsQIFS5cWJ9//rkMw8ihCgEAAJDb3LUR3uTkZM2aNUuxsbEqXLiwGjdurFq1ajm0iYmJ0alTpxQSEuI0bSFfvnxq3ry5vvvuOx06dEiVK1fOcD/Jycn258z7BQAAuL/dtcB75swZ9evXz2FZ27Zt9dVXX8nf31/S9cArKcMwa1seExOTYZuxY8cy5QEAAAB2d2VKw7PPPquoqCidO3dOCQkJ2rZtmx577DGtXLlSTzzxhH2KQnx8vKTrJ7ilx9fX16FdekaMGKH4+Hj748SJEy4+GgAAAOQmd2WE9+b5uA0bNtSyZcsUHByszZs364cfflC7du1csi+r1Sqr1eqSbQEAACD3y7GT1vLkyaP+/ftLkqKjoyX9b2Q3oxFc23zcjEaAAQAAgJvl6FUabHN3//77b0mOc3TTc7s5vgAAAMDNcjTw/ve//5V0/U5s0vUgGxAQoOjoaCUmJjq0TUpK0saNGxUQEKBKlSrd7VIBAACQS7k98P7666+6ePGi0/LNmzdrwoQJslqtevrppyVJFotFoaGhunz5ssaMGePQfuzYsYqLi1NoaKgsFou7ywYAAIBJuP2kta+//loRERFq3bq1AgMDZbVatW/fPq1evVp58uTRtGnTVK5cOXv74cOHa+nSpYqIiNCuXbtUr1497dmzRytWrFDt2rU1fPhwd5cMAAAAE3F74G3ZsqV+++037dy5Uxs2bFBSUpJKlCihrl27asiQIQoKCnJo7+3traioKI0ePVoLFy5UVFSUSpYsqSFDhigsLMzphhQAAADArbg98AYHBys4ODhLr/Hz89OECRM0YcIEN1UFAACA+0WOnrQGAAAAuBuBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpuf06vEBuFPjG8pwuAQAAuAgjvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1z5wuAAAAILcJfGN5TpfgdsfGtcvpElyGEV4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYmmdOF4DcJ/CN5TldAgAAQKYxwgsAAABTI/ACAADA1Ai8AAAAMDUCLwAAAEyNwAsAAABTI/ACAADA1Ai8AAAAMDUCLwAAAEyNwAsAAABTI/ACAADA1O7ZwLt9+3Y9/vjjKly4sLy9vRUUFKS5c+fmdFkAAADIZTxzuoD0REVFKSQkRF5eXurWrZv8/Py0ePFi9ezZU8eOHdObb76Z0yXeUuAby3O6BAAAAPy/e26ENzU1VaGhobJYLNq4caOmT5+uDz/8UHv27FH16tUVFhammJiYnC4TAAAAucQ9F3jXrVunw4cPq0ePHqpTp459ecGCBfXWW28pNTVVkZGROVghAAAAcpN7bkpDVFSUJOnRRx91WmdbtmHDhgxfn5ycrOTkZPvz+Ph4SVJCQoILq7y1tOS/79q+AOB+kJok/X350g3PE936WZuSkqSEm57fy5/tqVeuKCkh6Ybnfyst2cjBimAGdzM72fZlGO5531oMd205mzp37qyFCxdqx44dqlevntP6YsWKyWKx6OzZs+m+Pjw8XKNHj3Z3mQAAAHCxEydOqEyZMi7f7j0XeB999FGtWbNGMTExqlSpktP6ihUr6uTJkw6juDe6eYQ3LS1Nf/31l4oWLSqLxZLuaxISElS2bFmdOHFCvr6+rjkQSKJv3Ym+dR/61n3oW/ehb92HvnUfW9/+8ccfslgsCggIUJ48rp9xe89NabhTVqtVVqvVYVmhQoUy9VpfX1/eyG5C37oPfes+9K370LfuQ9+6D33rPn5+fm7t23vupDU/Pz9J/5t7e7OEhAR7GwAAAOB27rnAW7lyZUlK99JjcXFxOn/+vL0NAAAAcDv3XOANDg6WJK1evdppnW2ZrY2rWK1WhYWFOU2FwJ2jb92HvnUf+tZ96Fv3oW/dh751n7vVt/fcSWupqamqWrWqYmNjtW3bNtWuXVuSdOnSJT388MM6ePCg9u/frypVquRsoQAAAMgV7rnAK0nr169XSEiIrFarunfvLl9fXy1evFhHjx7Vu+++q5EjR+Z0iQAAAMgl7snAK0k//fSTwsLCtHXrVl29elXVq1fX4MGD1bNnz5wuDQAAALnIPRt4AQAAAFe4505aAwAAAFyJwAsAAABTM2Xg3b59ux5//HEVLlxY3t7eCgoK0ty5czP9+qioKPXo0UP/+Mc/VKhQIRUoUEBVq1bVs88+q4MHD7qx8nvfnfbtzVJSUlS7dm1ZLBZVq1bNhZXmTq5471oslgwf27Ztc2P19zZXvXcvXbqksLAwPfTQQypQoIAKFSqkunXravTo0W6oOne4075t0aLFLd+3FotFX331lRuP4N7livftxYsX9fbbb6tmzZoqWLCg/P391aBBA02dOlVJSUluqvze54q+PXnypAYMGKBy5crJy8tLAQEB6t+/v06cOOGmqu99s2fP1oABA1S/fn1ZrVZZLBbNnDkzy9tJS0vT1KlTVbNmTeXPn1/FihVTly5d0r1PQ2aY7tbCUVFRCgkJkZeXl7p16yY/Pz8tXrxYPXv21LFjx/Tmm2/edhtr167V5s2b1bBhQ/u2fvvtN3355ZeaO3euVqxYoZYtW96Fo7m3uKJvb/bOO+/o0KFDbqg293Fl/wYHB6tFixZOy8uUKePCinMPV/XtH3/8oVatWunIkSN65JFH1K5dOyUnJ+vQoUNatGiRwsLC3Hwk9x5X9G2/fv3Sfb+mpKRo7NixypMnj1q3bu2G6u9trujbixcvql69ejpy5IiaNm2qAQMGKDk5WStWrNCgQYP07bffas2aNcqTx5TjXxlyRd8ePnxYjRs31tmzZ9WmTRt17dpVMTExmjVrln744Qdt2bJFFStWvAtHc28ZNWqUjh8/Ln9/f5UqVUrHjx/P1nYGDhyo6dOn68EHH9SgQYP0559/asGCBVq9erW2bNmiBx98MGsbNEwkJSXFqFixomG1Wo2dO3falyckJBjVq1c3PD09jd9///2227ly5Uq6y9euXWtIMurXr++ymnMLV/XtjX7++WfD09PT+Pjjjw1JRtWqVV1ddq7hqv5dv369IckICwtzY7W5i6v6NjU11WjQoIGRP39+Y926denu537jjs+FGy1cuNCQZHTo0MEV5eYqrurb8ePHG5KMIUOGOCxPTk42GjRoYEgyNmzY4PL672Wu6tt27doZkozJkyc7LP/6668NSUZISIjLa88N1qxZYxw7dswwDMMYO3asIcmIjIzM0jbWrVtnSDKaNWtmJCUl2ZevXbvWsFgsRvPmzbNcl6kC76pVqwxJRv/+/Z3WzZ8/35BkjBgx4o72UbhwYaNQoUJ3tI3cyNV9m5ycbNSoUcNo2rSpkZaWdt8HXlf1L4HXmav61tb2rbfeckeZuZK7P3Pbtm1rSDKWLFlyJ2XmSq7q2wEDBhiSjDVr1jite/PNNw1JxjfffOOSmnMLV/TtlStXDE9PT6NEiRJGWlqa0/ratWsbkozDhw+7rO7cKLuBt3v37hn+MWb7XDh48GCWtmmq7zCioqIkSY8++qjTOtuyDRs2ZHv7W7duVVxcnB566KFsbyO3cnXfhoeHKyYmRp9//rksFotLaszNXN2/MTEx+vjjjzVu3DjNmzdP58+fd0mduZGr+nbBggWSpM6dO+vEiROaNm2axo0bp2+++UaXL192XcG5iDs/c0+ePKnVq1erZMmSateuXbZrzK1c1bfVq1eXJK1cudJheUpKitauXav8+fPr4YcfvsNqcxdX9O2FCxeUmpqq8uXLp/s7rEKFCpKu30gLWRcVFSVvb281adLEaV1ISIikrH+2mGoOr20ic+XKlZ3WFS5cWP7+/lma7BwVFaWoqCglJycrJiZGy5Ytk7+/vyZOnOiymnMLV/bt9u3bFRERoffff59bRP8/V793586d63DyRf78+TV69GgNGzbszovNZVzVtzt27JAkbd68WUOGDFFycrJ9XbFixfT111+nOw/VzFz9vr1RZGSk0tLS1K9fP3l6mupXVaa4qm9DQ0P11Vdf6aOPPtKOHTvUoEEDJScna+XKlYqLi9PcuXNVunRpl9d/L3NF3xYuXFgeHh46fvy4DMNwCr1Hjx6VJP3+++8uqvr+kZiYqNOnT+uhhx6Sh4eH03rbzy2rny2mGuGNj4+XJPn5+aW73tfX194mM6KiojR69GiNGzdOixYtUtmyZbVy5UrVr1/fJfXmJq7q2+TkZPXr10916tTRP//5T5fWmJu5qn+LFSumDz74QL/99psSExMVGxur2bNnq0iRIho+fLg+++wzl9adG7iqb8+ePStJGjRokAYPHqwTJ07o3Llz+vjjjxUfH6+OHTvq9OnTris8F3D1Z66NYRiKjIyUJD333HPZLzAXc1Xf5s+fX1FRUerVq5c2bNigDz/8UFOmTNHhw4fVo0cPNW3a1KV15wau6NsCBQooODhYf/75pz755BOHdYsXL9bu3bslXT9pEFmTmZ/Pje0yy1SB19XCw8NlGIYuX76sn376SdWqVVOTJk3u6DJc97u33npLMTEx+uKLL9L9yw13pnr16vrXv/6latWqqUCBAgoICFDPnj21cuVKeXl5KSwsTGlpaTldZq5k67f27dtr3LhxKlOmjPz9/TVo0CANGTJE8fHx+vzzz3O4SnNYt26djh49quDgYFWqVCmny8nVzp8/rzZt2mjbtm1avny5Ll68qDNnzmjatGmKjIxUw4YNFRcXl9Nl5koTJkyQj4+PXnnlFbVt21bDhw/X008/rc6dO6tmzZqSxO+5e4ipAq/tr4GMUn9CQkKGfzHcire3txo0aKBvv/1W1apV0wsvvKBz587dUa25jSv6dufOnZowYYJGjhypGjVquLzG3Mxd712bhx56SA0bNtSff/55310GzlV9a2vzxBNPOK3r0KGDpP9Ne7hfuOt9O2PGDEnXv46/X7mqb4cOHaotW7Zo0aJFevzxx+Xn56cSJUro+eefV0REhI4cOaJJkya5svR7nqv6tlatWtq+fbu6dOminTt3avLkyTp48KA+++wz9e7dW9L1b92QNZn5+dzYLrNMFXhvNa8jLi5O58+fT3fOTmZ5enqqZcuWSkxMvO9+sbmib/fu3atr164pPDzc6aLyknTw4EFZLBYVKlTI5fXf69z93pUkf39/SdLff/99R9vJbVzVt1WrVpWkdN+ftmVXrlzJfqG5kDvet3Fxcfr2229VqFAhPfPMMy6pMzdyVd8uX75cRYoUsY843qhVq1aSpJ9//vkOq81dXPm+rVatmhYsWKCzZ88qOTlZ+/fvV2hoqPbt2ydJ9+UUyDvl7e2tUqVK6ejRo7p27ZrT+lvNwb4VUwXe4OBgSdLq1aud1tmW2dpk16lTpyTpvjuJwhV9W6VKFT333HPpPqTrf60999xz6tOnj4urv/e5+72bmpqqnTt3ymKxqFy5ctneTm7kqr61hYNff/3VaZ1tWWBgYHbLzJXc8b6dPXu2kpOT1bNnT+XPn//Oi8ylXNW3V69eVUJCgq5eveq0zvZNpdVqvZNScx13f95eunRJ33//vYoUKaI2bdpkezv3s+DgYCUmJio6Otpp3apVq+xtsiRLFzG7x6WkpBgPPPCAYbVajV27dtmX33gx6Ruv23bu3Dnjt99+M86dO+ewnQ0bNqR7Xb1Vq1YZefPmNfz8/IzLly+77TjuRa7q24zoPr8Or6v6d8uWLU7v3ZSUFGPw4MGGJKNt27ZuPY57kav69siRI4bVajWKFy9unDx50mE7tmturl271u3Hcy9xx+dCrVq1DEkONwS4H7mqb0NCQgxJxqhRoxyWJyUl2ddNmTLFrcdyr3FV3/79999ON5xJSkoyOnfunO4NKe5Ht7sOb0Z9e+ONJ5KTk+3LufHEDdatW2fkzZvX8PHxMZ5//nnjn//8p1GhQgVDkvHuu+86tA0LC0v3Iv1+fn5GxYoVjW7duhnDhg0zXnnlFaN58+aGJCNv3rz33UW6bVzRtxm53wOvYbimf8uXL28EBgYaPXr0MIYNG2Y8//zzRtWqVQ1JRrly5ex3v7nfuOq9a7srYNGiRY3Q0FDj5ZdfNgIDAw1JxgsvvHCXjube4srPhR07dhiSjLp1696Fyu99rujbXbt2GQULFjQkGUFBQcaQIUOMF1980XjggQcMSUa9evUyvLuombmibzdt2mQUK1bM6NGjh/H6668bL774olGuXDlDkvH888+nO3B2P5g+fbrRt29fo2/fvkbdunUNSUaTJk3sy7799lt721t9JoSGhhqSjAcffNAYNmyY0adPH8NqtRp+fn7G/v37s1yX6QKvYRjGf//7X6Nt27aGn5+fkT9/fqN+/frG7Nmzndpl1NGTJk0y2rZta5QpU8awWq1Gvnz5jMqVKxuhoaHGvn377tJR3JvutG8zQuC97k77d9y4cUaLFi2MgIAAw8vLyyhQoIBRs2ZNY+TIkcZff/11l47i3uSq9+7SpUuNZs2aGT4+Pka+fPmMevXqGf/5z3/cXP29zVV9++KLLxqSjE8++cTNFecerujb33//3ejfv79Rrlw5I2/evEb+/PmNGjVqGKNHjzYSExPvwlHcm+60b48fP2507tzZKFu2rOHl5WUUKlTIaNWqlbFw4cK7dAT3pr59+xqSMnzc2I+3et9eu3bN+Pjjj43q1asbVqvVKFq0qNGpU6cs32HNxmIYhpG1SRAAAABA7mGqk9YAAACAmxF4AQAAYGoEXgAAAJgagRcAAACmRuAFAACAqRF4AQAAYGoEXgAAAJgagRcAAACmRuAFAACAqRF4AQAAYGoEXgAAAJgagRcAAACm9n/4mWvKwlcIRwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -1074,7 +1074,7 @@
      "data": {
       "text/html": [
        "\n",
-       "  <div class=\"bk-root\" id=\"69a35b52-bce3-4b28-b458-88da57a2e32a\" data-root-id=\"1401\"></div>\n"
+       "  <div class=\"bk-root\" id=\"a7d6c8a1-79ad-4f2e-a070-d62c1245755b\" data-root-id=\"1401\"></div>\n"
       ]
      },
      "metadata": {},
@@ -1085,8 +1085,8 @@
       "application/javascript": [
        "(function(root) {\n",
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-       "  const render_items = [{\"docid\":\"af27eaad-0568-4a6e-8b9b-602ef2fcbacf\",\"root_ids\":[\"1401\"],\"roots\":{\"1401\":\"69a35b52-bce3-4b28-b458-88da57a2e32a\"}}];\n",
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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1000]}},\"selected\":{\"id\":\"1382\"},\"selection_policy\":{\"id\":\"1381\"}},\"id\":\"1315\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1352\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1195\",\"type\":\"LinearScale\"},{\"attributes\":{\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        "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
        "  }\n",
        "  if (root.Bokeh !== undefined) {\n",
@@ -1937,10 +1937,10 @@
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           const bwFactor = widgets.bw_factor_slider.value;\\n            const hdiProbability = widgets.hdi_slider.value / 100;\\n            \\n            const rvName = widgets.rv_select.value;\\n            const rvData = data[rvName].flat();\\n            let bw = 0.0;\\n            // Remove the CSS classes that dim the tool output on initial load.\\n            const toolTab = toolView.tabs[0];\\n            const toolChildren = toolTab.child.children;\\n            const dimmedComponent = toolChildren[1];\\n            dimmedComponent.css_classes = [];\\n            try {\\n              bw = marginal1d.update(\\n                rvData,\\n                rvName,\\n                bwFactor,\\n                hdiProbability,\\n                sources,\\n                figures,\\n                tooltips,\\n              );\\n            } catch (error) {\\n              /******/ (() =&gt; { // webpackBootstrap\\n/******/ \\t\\\"use strict\\\";\\n/******/ \\t// The require scope\\n/******/ \\tvar __webpack_require__ = {};\\n/******/ \\t\\n/************************************************************************/\\n/******/ \\t/* webpack/runtime/define property getters */\\n/******/ \\t(() =&gt; {\\n/******/ \\t\\t// define getter functions for harmony exports\\n/******/ \\t\\t__webpack_require__.d = (exports, definition) =&gt; {\\n/******/ \\t\\t\\tfor(var key in definition) {\\n/******/ \\t\\t\\t\\tif(__webpack_require__.o(definition, key) &amp;&amp; !__webpack_require__.o(exports, key)) {\\n/******/ \\t\\t\\t\\t\\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\\n/******/ \\t\\t\\t\\t}\\n/******/ \\t\\t\\t}\\n/******/ \\t\\t};\\n/******/ \\t})();\\n/******/ \\t\\n/******/ \\t/* webpack/runtime/hasOwnProperty shorthand */\\n/******/ \\t(() =&gt; {\\n/******/ \\t\\t__webpack_require__.o = (obj, prop) =&gt; (Object.prototype.hasOwnProperty.call(obj, prop))\\n/******/ \\t})();\\n/******/ \\t\\n/******/ \\t/* webpack/runtime/make namespace object */\\n/******/ \\t(() =&gt; {\\n/******/ \\t\\t// define __esModule on exports\\n/******/ \\t\\t__webpack_require__.r = (exports) =&gt; {\\n/******/ \\t\\t\\tif(typeof Symbol !== 'undefined' &amp;&amp; Symbol.toStringTag) {\\n/******/ \\t\\t\\t\\tObject.defineProperty(exports, Symbol.toStringTag, { value: 'Module' });\\n/******/ \\t\\t\\t}\\n/******/ \\t\\t\\tObject.defineProperty(exports, '__esModule', { value: true });\\n/******/ \\t\\t};\\n/******/ \\t})();\\n/******/ \\t\\n/************************************************************************/\\nvar __webpack_exports__ = {};\\n\\n// NAMESPACE OBJECT: ./src/marginal1d/callbacks.ts\\nvar callbacks_namespaceObject = {};\\n__webpack_require__.r(callbacks_namespaceObject);\\n__webpack_require__.d(callbacks_namespaceObject, {\\n  \\\"computeData\\\": () =&gt; (computeData),\\n  \\\"computeStats\\\": () =&gt; (computeStats),\\n  \\\"update\\\": () =&gt; (update),\\n  \\\"updateAxisLabel\\\": () =&gt; (updateAxisLabel)\\n});\\n\\n;// CONCATENATED MODULE: ./src/stats/array.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n/**\\n * Cumulative sum of the given data.\\n *\\n * @param {number[]} data - Any array of data.\\n * @returns {number[]} The cumulative sum of the given data.\\n */\\nconst cumulativeSum = (data) =&gt; {\\n    // eslint-disable-next-line arrow-body-style\\n    const cumulativeSumMap = ((sum) =&gt; (value) =&gt; {\\n        // eslint-disable-next-line no-return-assign\\n        return (sum += value);\\n    })(0);\\n    const cSum = data.map(cumulativeSumMap);\\n    const normalizationFactor = Math.max(...cSum);\\n    const cumSum = [];\\n    for (let i = 0; i &lt; cSum.length; i += 1) {\\n        cumSum.push(cSum[i] / normalizationFactor);\\n    }\\n    return cumSum;\\n};\\n/**\\n * Numerically sort the given array of numbers.\\n *\\n * @param {number[]} data - The array of numbers to sort.\\n * @returns {number[]} The sorted array of numbers.\\n */\\nconst numericalSort = (data) =&gt; {\\n    const dataCopy = data.slice(0);\\n    return dataCopy.sort((a, b) =&gt; {\\n        return a &lt; b ? -1 : a &gt; b ? 1 : 0;\\n    });\\n};\\n/**\\n * Determine the shape of the given array.\\n *\\n * @param {any[]} data - Any array of data.\\n * @returns {number[]} The shape of the data as an array.\\n */\\nconst shape = (data) =&gt; {\\n    // From https://stackoverflow.com/questions/10237615/get-size-of-dimensions-in-array\\n    const computeShape = (array) =&gt; {\\n        return array.length ? [...[array.length], ...computeShape(array[0])] : [];\\n    };\\n    const arrayShape = computeShape(data);\\n    // Remove the empty array that will exist at the end of the shape array, since it is\\n    // the returned \\\"else\\\" value from above.\\n    const dataShape = [];\\n    for (let i = 0; i &lt; arrayShape.length; i += 1) {\\n        if (!Array.isArray(arrayShape[i])) {\\n            dataShape.push(arrayShape[i]);\\n        }\\n    }\\n    return dataShape;\\n};\\n/**\\n * Create an array that starts and stops with the given number of steps.\\n *\\n * @param {number} start - Where to start the array from.\\n * @param {number} stop - Where to stop the array.\\n * @param {number} [step] - The step size to take.\\n * @param {boolean} [closed] - Flag used to return a closed array or not.\\n * @param {null | number} [size] - If not null, then will return an array with the given\\n *     size.\\n * @returns {number[]} An array that is linearly spaced between the start and stop\\n *     values.\\n */\\nconst linearRange = (start, stop, step = 1, closed = true, size = null) =&gt; {\\n    if (size !== null) {\\n        step = (stop - start) / size;\\n    }\\n    let len = (stop - start) / step + 1;\\n    if (!closed) {\\n        len = (stop - start - step) / step + 1;\\n    }\\n    return Array.from({ length: len }, (_, i) =&gt; {\\n        return start + i * step;\\n    });\\n};\\n/**\\n * Return the indices that would sort the array. Follows NumPy's implementation.\\n *\\n * @param {number[]} data - The data to sort.\\n * @returns {number[]} An array of indices that would sort the original array.\\n */\\nconst array_argSort = (data) =&gt; {\\n    const dataCopy = data.slice(0);\\n    return dataCopy\\n        .map((value, index) =&gt; {\\n        return [value, index];\\n    })\\n        .sort((a, b) =&gt; {\\n        return a[0] - b[0];\\n    })\\n        .map((value) =&gt; {\\n        return value[1];\\n    });\\n};\\n/**\\n * Count the number of time a value appears in an array.\\n *\\n * @param {number[]} data - The numeric array to count objects for.\\n * @returns {{[key: string]: number}} An object that contains the keys as the items in\\n *     the original array, and values that are counts of the key.\\n */\\nconst array_valueCounts = (data) =&gt; {\\n    const counts = {};\\n    for (let i = 0; i &lt; data.length; i += 1) {\\n        counts[data[i]] = (counts[data[i]] || 0) + 1;\\n    }\\n    return counts;\\n};\\n\\n;// CONCATENATED MODULE: ./src/stats/dataTransformation.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n/**\\n * Scale the given array of numbers by the given scaleFactor. Note that this method\\n * divides values in the given array by the scaleFactor.\\n *\\n * @param {number[]} data - An array of numbers that needs to be scaled.\\n * @param {number} scaleFactor - The value to divide all array values by.\\n * @returns {number[]} The scaled array.\\n */\\nconst scaleBy = (data, scaleFactor) =&gt; {\\n    const scaledData = data.map((datum) =&gt; {\\n        return datum / scaleFactor;\\n    });\\n    return scaledData;\\n};\\n/**\\n * Scale the given array to a maximum value of 1. This method uses the scaleBy method,\\n * and finds the maximum value of the given array to use as the scaleFactor of the\\n * scaleBy method.\\n *\\n * @param {number[]} data - Array of data to scale to one.\\n * @returns {number[]} The data scaled to one.\\n */\\nconst scaleToOne = (data) =&gt; {\\n    const scaleFactor = Math.max(...data);\\n    return scaleBy(data, scaleFactor);\\n};\\n/**\\n * Assign ranks to the given data. Follows SciPy's and ArviZ's implementations.\\n *\\n * @param {number[]} data - The numeric data to rank.\\n * @returns {number[]} An array of rankings.\\n */\\nconst rankData = (data) =&gt; {\\n    const n = data.length;\\n    const rank = Array(n);\\n    const sortedIndex = argSort(data);\\n    for (let i = 0; i &lt; rank.length; i += 1) {\\n        rank[sortedIndex[i]] = i + 1;\\n    }\\n    const counts = valueCounts(data);\\n    const countsArray = Object.entries(counts);\\n    const keys = [];\\n    const keyCounts = [];\\n    for (let i = 0; i &lt; countsArray.length; i += 1) {\\n        const [key, count] = countsArray[i];\\n        if (count &gt; 1) {\\n            keys.push(parseFloat(key));\\n            keyCounts.push(count);\\n        }\\n    }\\n    for (let i = 0; i &lt; keys.length; i += 1) {\\n        const repeatIndices = [];\\n        for (let j = 0; j &lt; data.length; j += 1) {\\n            if (data[j] === keys[i]) {\\n                repeatIndices.push(j);\\n            }\\n        }\\n        const rankValues = [];\\n        for (let k = 0; k &lt; repeatIndices.length; k += 1) {\\n            rankValues.push(rank[repeatIndices[k]]);\\n        }\\n        const sum = rankValues.reduce((previousValue, currentValue) =&gt; {\\n            return previousValue + currentValue;\\n        }, 0.0);\\n        const rankMean = sum / rankValues.length;\\n        for (let k = 0; k &lt; repeatIndices.length; k += 1) {\\n            rank[repeatIndices[k]] = rankMean;\\n        }\\n    }\\n    return rank;\\n};\\n\\n;// CONCATENATED MODULE: ./src/stats/highestDensityInterval.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n/**\\n * Find the lower and upper bound of the Highest Density Interval (HDI) for the given\\n * data.\\n *\\n * @param {number[]} data - Raw random variable data from the model.\\n * @param {number} hdiProbability - The highest density interval probability to use when\\n *     calculating the HDI.\\n * @returns {HdiInterval} An object defining the lower and upper bound for the HDI of\\n *     the given data, as well as the indices of the lower and upper bound for the\\n *     sorted raw data.\\n */\\nconst interval = (data, hdiProbability) =&gt; {\\n    const N = data.length;\\n    const sortedData = numericalSort(data);\\n    const stopIndex = Math.floor(hdiProbability * N);\\n    const startIndex = N - stopIndex;\\n    const leftData = sortedData.slice(stopIndex);\\n    const rightData = sortedData.slice(0, startIndex);\\n    const hdi = [];\\n    for (let i = 0; i &lt; leftData.length; i += 1) {\\n        hdi.push(leftData[i] - rightData[i]);\\n    }\\n    const lowerIndex = hdi.indexOf(Math.min(...hdi));\\n    const upperIndex = lowerIndex + stopIndex;\\n    return {\\n        lowerBound: sortedData[lowerIndex],\\n        upperBound: sortedData[upperIndex],\\n        lowerBoundIndex: lowerIndex,\\n        upperBoundIndex: upperIndex,\\n    };\\n};\\n/**\\n * Constructs x and y arrays from the HDI bounds using the raw random variable data. It\\n * also returns the actual HDI bounds of the random variable data.\\n *\\n * @param {number[]} rvData - Raw random variable data from the model.\\n * @param {number[]} marginalX - The support of the Kernel Density Estimate of the\\n *     random variable.\\n * @param {number[]} marginalY - The Kernel Density Estimate of the random variable.\\n * @param {number} hdiProbability - The highest density interval probability to use when\\n *     calculating the HDI.\\n * @returns {HdiData} The x array for the HDI data is the \\\"lower\\\" key, while the y array\\n *     for the HDI data is the \\\"upper\\\" key and the \\\"base\\\" key is the support along the\\n *     axis the HDI is calculated. This nomenclature mirrors the Bokeh Band object,\\n *     which is the annotation type we will use when rendering the HDI intervals.\\n */\\nconst highestDensityInterval_data = (rvData, marginalX, marginalY, hdiProbability) =&gt; {\\n    const hdi = interval(rvData, hdiProbability);\\n    const base = [];\\n    const upper = [];\\n    for (let i = 0; i &lt; marginalX.length; i += 1) {\\n        if (marginalX[i] &lt;= hdi.upperBound &amp;&amp; marginalX[i] &gt;= hdi.lowerBound) {\\n            base.push(marginalX[i]);\\n            upper.push(marginalY[i]);\\n        }\\n    }\\n    // NOTE: We mirror the Bokeh Band annotation object key names below. The lower key is\\n    //       an array of zeros since we will fill the Band all the way to the lower axis.\\n    return {\\n        base: base,\\n        lower: new Array(base.length).fill(0.0),\\n        upper: upper,\\n        lowerBound: hdi.lowerBound,\\n        upperBound: hdi.upperBound,\\n    };\\n};\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/accessor.js\\nfunction accessor(x, fallback) {\\n  return x == null ? fallback\\n    : typeof x === 'function' ? x\\n    : d =&gt; d[x];\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/bin1d.js\\nfunction bin1d(data, x, weight, lo, hi, n) {\\n  const grid = new Float64Array(n);\\n  const delta = (n - 1) / (hi - lo);\\n\\n  for (let i = 0; i &lt; data.length; ++i) {\\n    const d = data[i];\\n    const xi = x(d, i, data);\\n    const wi = weight(d, i, data);\\n\\n    // skip NaN and Infinite values\\n    if (!(Number.isFinite(xi) &amp;&amp; Number.isFinite(wi))) {\\n      continue;\\n    }\\n\\n    const p = (xi - lo) * delta;\\n    const u = Math.floor(p);\\n    const v = u + 1;\\n\\n    if (0 &lt;= u &amp;&amp; v &lt; n) {\\n      grid[u] += (v - p) * wi;\\n      grid[v] += (p - u) * wi;\\n    } else if (u === -1) {\\n      grid[v] += (p - u) * wi;\\n    } else if (v === n) {\\n      grid[u] += (v - p) * wi;\\n    }\\n  }\\n\\n  return grid;\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/deriche.js\\n// Deriche's approximation of Gaussian smoothing\\n// Adapted from Getreuer's C implementation (BSD license)\\n// https://www.ipol.im/pub/art/2013/87/gaussian_20131215.tgz\\n// http://dev.ipol.im/~getreuer/code/doc/gaussian_20131215_doc/gaussian__conv__deriche_8c.html\\n\\nfunction dericheConfig(sigma, negative = false) {\\n  // compute causal filter coefficients\\n  const a = new Float64Array(5);\\n  const bc = new Float64Array(4);\\n  dericheCausalCoeff(a, bc, sigma);\\n\\n  // numerator coefficients of the anticausal filter\\n  const ba = Float64Array.of(\\n    0,\\n    bc[1] - a[1] * bc[0],\\n    bc[2] - a[2] * bc[0],\\n    bc[3] - a[3] * bc[0],\\n    -a[4] * bc[0]\\n  );\\n\\n  // impulse response sums\\n  const accum_denom = 1.0 + a[1] + a[2] + a[3] + a[4];\\n  const sum_causal = (bc[0] + bc[1] + bc[2] + bc[3]) / accum_denom;\\n  const sum_anticausal = (ba[1] + ba[2] + ba[3] + ba[4]) / accum_denom;\\n\\n  // coefficients object\\n  return {\\n    sigma,\\n    negative,\\n    a,\\n    b_causal: bc,\\n    b_anticausal: ba,\\n    sum_causal,\\n    sum_anticausal\\n  };\\n}\\n\\nfunction dericheCausalCoeff(a_out, b_out, sigma) {\\n  const K = 4;\\n\\n  const alpha = Float64Array.of(\\n    0.84, 1.8675,\\n    0.84, -1.8675,\\n    -0.34015, -0.1299,\\n    -0.34015, 0.1299\\n  );\\n\\n  const x1 = Math.exp(-1.783 / sigma);\\n  const x2 = Math.exp(-1.723 / sigma);\\n  const y1 = 0.6318 / sigma;\\n  const y2 = 1.997 / sigma;\\n  const beta = Float64Array.of(\\n    -x1 * Math.cos( y1), x1 * Math.sin( y1),\\n    -x1 * Math.cos(-y1), x1 * Math.sin(-y1),\\n    -x2 * Math.cos( y2), x2 * Math.sin( y2),\\n    -x2 * Math.cos(-y2), x2 * Math.sin(-y2)\\n  );\\n\\n  const denom = sigma * 2.5066282746310007;\\n\\n  // initialize b/a = alpha[0] / (1 + beta[0] z^-1)\\n  const b = Float64Array.of(alpha[0], alpha[1], 0, 0, 0, 0, 0, 0);\\n  const a = Float64Array.of(1, 0, beta[0], beta[1], 0, 0, 0, 0, 0, 0);\\n\\n  let j, k;\\n\\n  for (k = 2; k &lt; 8; k += 2) {\\n    // add kth term, b/a += alpha[k] / (1 + beta[k] z^-1)\\n    b[k]     = beta[k] * b[k - 2] - beta[k + 1] * b[k - 1];\\n    b[k + 1] = beta[k] * b[k - 1] + beta[k + 1] * b[k - 2];\\n    for (j = k - 2; j &gt; 0; j -= 2) {\\n      b[j]     += beta[k] * b[j - 2] - beta[k + 1] * b[j - 1];\\n      b[j + 1] += beta[k] * b[j - 1] + beta[k + 1] * b[j - 2];\\n    }\\n    for (j = 0; j &lt;= k; j += 2) {\\n      b[j]     += alpha[k] * a[j]     - alpha[k + 1] * a[j + 1];\\n      b[j + 1] += alpha[k] * a[j + 1] + alpha[k + 1] * a[j];\\n    }\\n\\n    a[k + 2] = beta[k] * a[k]     - beta[k + 1] * a[k + 1];\\n    a[k + 3] = beta[k] * a[k + 1] + beta[k + 1] * a[k];\\n    for (j = k; j &gt; 0; j -= 2) {\\n      a[j]     += beta[k] * a[j - 2] - beta[k + 1] * a[j - 1];\\n      a[j + 1] += beta[k] * a[j - 1] + beta[k + 1] * a[j - 2];\\n    }\\n  }\\n\\n  for (k = 0; k &lt; K; ++k) {\\n    j = k &lt;&lt; 1;\\n    b_out[k] = b[j] / denom;\\n    a_out[k + 1] = a[j + 2];\\n  }\\n}\\n\\nfunction dericheConv2d(cx, cy, grid, [nx, ny]) {\\n  // allocate buffers\\n  const yc = new Float64Array(Math.max(nx, ny)); // causal\\n  const ya = new Float64Array(Math.max(nx, ny)); // anticausal\\n  const h = new Float64Array(5);\\n  const d = new Float64Array(grid.length);\\n\\n  // convolve rows\\n  for (let row = 0, r0 = 0; row &lt; ny; ++row, r0 += nx) {\\n    const dx = d.subarray(r0);\\n    dericheConv1d(cx, grid.subarray(r0), nx, 1, yc, ya, h, dx);\\n  }\\n\\n  // convolve columns\\n  for (let c0 = 0; c0 &lt; nx; ++c0) {\\n    const dy = d.subarray(c0);\\n    dericheConv1d(cy, dy, ny, nx, yc, ya, h, dy);\\n  }\\n\\n  return d;\\n}\\n\\nfunction dericheConv1d(\\n  c, src, N,\\n  stride = 1,\\n  y_causal = new Float64Array(N),\\n  y_anticausal = new Float64Array(N),\\n  h = new Float64Array(5),\\n  d = y_causal,\\n  init = dericheInitZeroPad\\n) {\\n  const stride_2 = stride * 2;\\n  const stride_3 = stride * 3;\\n  const stride_4 = stride * 4;\\n  const stride_N = stride * N;\\n  let i, n;\\n\\n  // initialize causal filter on the left boundary\\n  init(\\n    y_causal, src, N, stride,\\n    c.b_causal, 3, c.a, 4, c.sum_causal, h, c.sigma\\n  );\\n\\n  // filter the interior samples using a 4th order filter. Implements:\\n  // for n = K, ..., N - 1,\\n  //   y^+(n) = \\\\sum_{k=0}^{K-1} b^+_k src(n - k)\\n  //          - \\\\sum_{k=1}^K a_k y^+(n - k)\\n  // variable i tracks the offset to the nth sample of src, it is\\n  // updated together with n such that i = stride * n.\\n  for (n = 4, i = stride_4; n &lt; N; ++n, i += stride) {\\n    y_causal[n] = c.b_causal[0] * src[i]\\n      + c.b_causal[1] * src[i - stride]\\n      + c.b_causal[2] * src[i - stride_2]\\n      + c.b_causal[3] * src[i - stride_3]\\n      - c.a[1] * y_causal[n - 1]\\n      - c.a[2] * y_causal[n - 2]\\n      - c.a[3] * y_causal[n - 3]\\n      - c.a[4] * y_causal[n - 4];\\n  }\\n\\n  // initialize the anticausal filter on the right boundary\\n  init(\\n    y_anticausal, src, N, -stride,\\n    c.b_anticausal, 4, c.a, 4, c.sum_anticausal, h, c.sigma\\n  );\\n\\n  // similar to the causal filter above, the following implements:\\n  // for n = K, ..., N - 1,\\n  //   y^-(n) = \\\\sum_{k=1}^K b^-_k src(N - n - 1 - k)\\n  //          - \\\\sum_{k=1}^K a_k y^-(n - k)\\n  // variable i is updated such that i = stride * (N - n - 1).\\n  for (n = 4, i = stride_N - stride * 5; n &lt; N; ++n, i -= stride) {\\n    y_anticausal[n] = c.b_anticausal[1] * src[i + stride]\\n      + c.b_anticausal[2] * src[i + stride_2]\\n      + c.b_anticausal[3] * src[i + stride_3]\\n      + c.b_anticausal[4] * src[i + stride_4]\\n      - c.a[1] * y_anticausal[n - 1]\\n      - c.a[2] * y_anticausal[n - 2]\\n      - c.a[3] * y_anticausal[n - 3]\\n      - c.a[4] * y_anticausal[n - 4];\\n  }\\n\\n  // sum the causal and anticausal responses to obtain the final result\\n  if (c.negative) {\\n    // do not threshold if the input grid includes negatively weighted values\\n    for (n = 0, i = 0; n &lt; N; ++n, i += stride) {\\n      d[i] = y_causal[n] + y_anticausal[N - n - 1];\\n    }\\n  } else {\\n    // threshold to prevent small negative values due to floating point error\\n    for (n = 0, i = 0; n &lt; N; ++n, i += stride) {\\n      d[i] = Math.max(0, y_causal[n] + y_anticausal[N - n - 1]);\\n    }\\n  }\\n\\n  return d;\\n}\\n\\nfunction dericheInitZeroPad(dest, src, N, stride, b, p, a, q, sum, h) {\\n  const stride_N = Math.abs(stride) * N;\\n  const off = stride &lt; 0 ? stride_N + stride : 0;\\n  let i, n, m;\\n\\n  // compute the first q taps of the impulse response, h_0, ..., h_{q-1}\\n  for (n = 0; n &lt; q; ++n) {\\n    h[n] = (n &lt;= p) ? b[n] : 0;\\n    for (m = 1; m &lt;= q &amp;&amp; m &lt;= n; ++m) {\\n      h[n] -= a[m] * h[n - m];\\n    }\\n  }\\n\\n  // compute dest_m = sum_{n=1}^m h_{m-n} src_n, m = 0, ..., q-1\\n  // note: q == 4\\n  for (m = 0; m &lt; q; ++m) {\\n    for (dest[m] = 0, n = 1; n &lt;= m; ++n) {\\n      i = off + stride * n;\\n      if (i &gt;= 0 &amp;&amp; i &lt; stride_N) {\\n        dest[m] += h[m - n] * src[i];\\n      }\\n    }\\n  }\\n\\n  // dest_m = dest_m + h_{n+m} src_{-n}\\n  const cur = src[off];\\n  if (cur &gt; 0) {\\n    for (m = 0; m &lt; q; ++m) {\\n      dest[m] += h[m] * cur;\\n    }\\n  }\\n\\n  return;\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/extent.js\\nfunction extent(data, x, pad = 0) {\\n  const n = data.length;\\n  let lo;\\n  let hi;\\n  for (let i = 0; i &lt; n; ++i) {\\n    const v = x(data[i], i, data);\\n    if (v != null) {\\n      if (lo === undefined) {\\n        if (v &gt;= v) lo = hi = v;\\n      } else {\\n        if (v &lt; lo) lo = v;\\n        if (v &gt; hi) hi = v;\\n      }\\n    }\\n  }\\n  return [lo - pad, hi + pad];\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/nrd.js\\n// Scott, D. W. (1992) Multivariate Density Estimation:\\n// Theory, Practice, and Visualization. Wiley.\\nfunction nrd(data, x) {\\n  const values = data.map(x).filter(v =&gt; v != null &amp;&amp; v &gt;= v);\\n  values.sort((a, b) =&gt; a - b);\\n  const sd = stdev(values);\\n  const q1 = quantile(values, 0.25);\\n  const q3 = quantile(values, 0.75);\\n\\n  const n = values.length,\\n        h = (q3 - q1) / 1.34,\\n        v = Math.min(sd, h) || sd || Math.abs(q1) || 1;\\n\\n  return 1.06 * v * Math.pow(n, -0.2);\\n}\\n\\nfunction stdev(values) {\\n  const n = values.length;\\n  let count = 0;\\n  let delta;\\n  let mean = 0;\\n  let sum = 0;\\n  for (let i = 0; i &lt; n; ++i) {\\n    const value = values[i];\\n    delta = value - mean;\\n    mean += delta / ++count;\\n    sum += delta * (value - mean);\\n  }\\n  return count &gt; 1 ? Math.sqrt(sum / (count - 1)) : NaN;\\n}\\n\\nfunction quantile(values, p) {\\n  const n = values.length;\\n\\n  if (!n) return NaN;\\n  if ((p = +p) &lt;= 0 || n &lt; 2) return values[0];\\n  if (p &gt;= 1) return values[n - 1];\\n\\n  const i = (n - 1) * p;\\n  const i0 = Math.floor(i);\\n  const v0 = values[i0];\\n  return v0 + (values[i0 + 1] - v0) * (i - i0);\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/density1d.js\\n\\n\\n\\n\\n\\n\\nfunction density1d(data, options = {}) {\\n  const { adjust = 1, pad = 3, bins = 512 } = options;\\n  const x = accessor(options.x, x =&gt; x);\\n  const w = accessor(options.weight, () =&gt; 1 / data.length);\\n\\n  let bandwidth = options.bandwidth ?? adjust * nrd(data, x);\\n\\n  const [lo, hi] = options.extent ?? extent(data, x, pad * bandwidth);\\n  const grid = bin1d(data, x, w, lo, hi, bins);\\n  const delta = (hi - lo) / (bins - 1);\\n  const neg = grid.some(v =&gt; v &lt; 0);\\n\\n  let config = dericheConfig(bandwidth / delta, neg);\\n  let result;\\n\\n  function* points(x = 'x', y = 'y') {\\n    const result = estimator.grid();\\n    const scale = 1 / delta;\\n    for (let i = 0; i &lt; bins; ++i) {\\n      yield {\\n        [x]: lo + i * delta,\\n        [y]: result[i] * scale\\n      };\\n    }\\n  }\\n\\n  const estimator = {\\n    [Symbol.iterator]: points,\\n    points,\\n    grid: () =&gt; result || (result = dericheConv1d(config, grid, bins)),\\n    extent: () =&gt; [lo, hi],\\n    bandwidth(_) {\\n      if (arguments.length) {\\n        if (_ !== bandwidth) {\\n          bandwidth = _;\\n          result = null;\\n          config = dericheConfig(bandwidth / delta, neg);\\n        }\\n        return estimator;\\n      } else {\\n        return bandwidth;\\n      }\\n    }\\n  };\\n\\n  return estimator;\\n}\\n\\n;// CONCATENATED MODULE: ./src/stats/marginal.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n\\n/**\\n * Calculate the one-dimensional Kernel Density Estimate. This method uses the \\\"scott\\\"\\n * estimate for the bandwidth used when calculating the marginal. This bandwidth\\n * estimate is the same as is used in the corresponding Python KDE estimate from ArviZ.\\n *\\n * Scott, D (1979). On optimal and data-based histograms. Biometrika. 66 (3): 605\\u2013610.\\n * doi:10.1093/biomet/66.3.605.\\n *\\n * @param {number[]} data - The raw random variable data of the model.\\n * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when\\n *     calculating the Kernel Density Estimate (KDE).\\n * @returns {{x: number[]; y: number[]; bandwidth: number}} The KDE of the given data,\\n *     along with the numerical value of the bandwidth used to calculate the KDE.\\n */\\n// eslint-disable-next-line import/prefer-default-export\\nconst oneD = (data, bwFactor) =&gt; {\\n    // Calculate the 1D KDE.\\n    const kde1d = density1d(data, {\\n        adjust: bwFactor,\\n        pad: 3,\\n        bins: 512,\\n    });\\n    // Extract the data points from the 1D KDE calculation.\\n    const points = Array.from(kde1d);\\n    const x = [];\\n    const y = [];\\n    for (let i = 0; i &lt; points.length; i += 1) {\\n        x.push(points[i].x);\\n        y.push(points[i].y);\\n    }\\n    return {\\n        x: x,\\n        y: scaleToOne(y),\\n        bandwidth: kde1d.bandwidth(),\\n    };\\n};\\n\\n;// CONCATENATED MODULE: ./src/stats/pointStatistic.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n/**\\n * Syntactic sugar for summing an array of numbers.\\n *\\n * @param {number[]} data - The array of data.\\n * @returns {number} The sum of the array of data.\\n */\\nconst sum = (data) =&gt; {\\n    return data.reduce((previousValue, currentValue) =&gt; {\\n        return previousValue + currentValue;\\n    });\\n};\\n/**\\n * Calculate the mean of the given array of data.\\n *\\n * @param {number[]} data - The array of data.\\n * @returns {number} The mean of the given data.\\n */\\nconst pointStatistic_mean = (data) =&gt; {\\n    const dataSum = sum(data);\\n    return dataSum / data.length;\\n};\\n\\n;// CONCATENATED MODULE: ./src/stats/utils.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\nconst left_edge_index = (point, intervals) =&gt; {\\n    const len = intervals.length;\\n    if (point &lt; intervals[0]) {\\n        return -1;\\n    }\\n    if (point &gt; intervals[len - 1]) {\\n        return intervals.length;\\n    }\\n    let leftEdgeIndex = 0;\\n    let rightEdgeIndex = len - 1;\\n    while (rightEdgeIndex - leftEdgeIndex !== 1) {\\n        const indexOfNumberToCompare = leftEdgeIndex + Math.floor((rightEdgeIndex - leftEdgeIndex) / 2);\\n        if (point &gt;= intervals[indexOfNumberToCompare])\\n            leftEdgeIndex = indexOfNumberToCompare;\\n        else\\n            rightEdgeIndex = indexOfNumberToCompare;\\n    }\\n    return leftEdgeIndex;\\n};\\nconst lerp = (x, x0, y0, x1, y1) =&gt; {\\n    const slope = (y1 - y0) / (x1 - x0);\\n    let res = slope * (x - x0) + y0;\\n    if (!Number.isFinite(res)) {\\n        res = slope * (x - x1) + y1;\\n        if (!Number.isFinite(res) &amp;&amp; y0 === y1)\\n            res = y0;\\n    }\\n    return res;\\n};\\nconst interpolate = (points, x, y) =&gt; {\\n    // Implementation ported from np.interp\\n    const n = points.length;\\n    const results = new Array(n);\\n    for (let i = 0; i &lt; n; i += 1) {\\n        const point = points[i];\\n        if (Number.isNaN(point)) {\\n            results[i] = point;\\n            // eslint-disable-next-line no-continue\\n            continue;\\n        }\\n        const index = left_edge_index(point, x);\\n        if (index === -1) {\\n            // eslint-disable-next-line prefer-destructuring\\n            results[i] = y[0];\\n        }\\n        else if (index === x.length) {\\n            results[i] = y[y.length - 1];\\n        }\\n        else if (index === x.length - 1 || x[index] === point) {\\n            results[i] = y[index];\\n        }\\n        else {\\n            const x0 = x[index];\\n            const y0 = y[index];\\n            const x1 = x[index + 1];\\n            const y1 = y[index + 1];\\n            results[i] = lerp(point, x0, y0, x1, y1);\\n        }\\n    }\\n    return results;\\n};\\n// eslint-disable-next-line import/prefer-default-export\\nconst interpolatePoints = ({ x, y, points, }) =&gt; {\\n    const output = [];\\n    for (let i = 0; i &lt; points.length; i += 1) {\\n        const interpolatedPoint = Array.from(interpolate([points[i]], x, y));\\n        output.push(interpolatedPoint[0]);\\n    }\\n    return output;\\n};\\n\\n;// CONCATENATED MODULE: ./src/marginal1d/callbacks.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n\\n\\n\\n\\n\\n// Define the names of the figures used for this Bokeh application.\\nconst figureNames = ['marginal', 'cumulative'];\\n/**\\n * Update the given Bokeh Axis object with the new label string. You must use this\\n * method to update axis strings using TypeScript, otherwise the ts compiler will throw\\n * a type check error.\\n *\\n * @param {Axis} axis - The Bokeh Axis object needing a new label.\\n * @param {string | null} label - The new label for the Bokeh Axis object.\\n */\\nconst updateAxisLabel = (axis, label) =&gt; {\\n    // Type check requirement.\\n    if ('axis_label' in axis) {\\n        axis.axis_label = label;\\n    }\\n};\\n/**\\n * Compute the following statistics for the given random variable data\\n *\\n * - lower bound for the highest density interval calculated from the marginalX;\\n * - mean of the rawData;\\n * - upper bound for the highest density interval calculated from the marginalY.\\n *\\n * @param {number[]} rawData - Raw random variable data from the model.\\n * @param {number[]} marginalX - The support of the Kernel Density Estimate of the\\n *     random variable.\\n * @param {number[]} marginalY - The Kernel Density Estimate of the random variable.\\n * @param {number | null} [hdiProb=null] - The highest density interval probability\\n *     value. If the default value is not overwritten, then the default HDI probability\\n *     is 0.89. See Statistical Rethinking by McElreath for a description as to why this\\n *     value is the default.\\n * @param {string[]} [text_align=['right', 'center', 'left']] - How to align the text\\n *     shown in the figure for the point statistics.\\n * @param {number[]} [x_offset=[-5, 0, 5]] - Offset values for the text along the\\n *     x-axis.\\n * @param {number[]} [y_offset=[0, 10, 0]] - Offset values for the text along the\\n *     y-axis\\n * @returns {interfaces.LabelsData} Object containing the computed stats.\\n */\\nconst computeStats = (rawData, marginalX, marginalY, hdiProb = null, text_align = ['right', 'center', 'left'], x_offset = [-5, 0, 5], y_offset = [0, 10, 0]) =&gt; {\\n    // Set the default value to 0.89 if no default value has been given.\\n    const hdiProbability = hdiProb ?? 0.89;\\n    // Compute the point statistics for the KDE, and create labels to display them in the\\n    // figures.\\n    const mean = pointStatistic_mean(rawData);\\n    const hdiBounds = interval(rawData, hdiProbability);\\n    const x = [hdiBounds.lowerBound, mean, hdiBounds.upperBound];\\n    const y = interpolatePoints({ x: marginalX, y: marginalY, points: x });\\n    const text = [\\n        `Lower HDI: ${hdiBounds.lowerBound.toFixed(3)}`,\\n        `Mean: ${mean.toFixed(3)}`,\\n        `Upper HDI: ${hdiBounds.upperBound.toFixed(3)}`,\\n    ];\\n    return {\\n        x: x,\\n        y: y,\\n        text: text,\\n        text_align: text_align,\\n        x_offset: x_offset,\\n        y_offset: y_offset,\\n    };\\n};\\n/**\\n * Compute data for the one-dimensional marginal diagnostic tool.\\n *\\n * @param {number[]} data - Raw random variable data from the model.\\n * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when\\n *     calculating the Kernel Density Estimate (KDE).\\n * @param {number} hdiProbability - The highest density interval probability to use when\\n *     calculating the HDI.\\n * @returns {interfaces.Data} The marginal distribution and cumulative\\n *     distribution calculated from the given random variable data. Point statistics are\\n *     also calculated.\\n */\\nconst computeData = (data, bwFactor, hdiProbability) =&gt; {\\n    const output = {};\\n    for (let i = 0; i &lt; figureNames.length; i += 1) {\\n        const figureName = figureNames[i];\\n        output[figureName] = {};\\n        // Compute the one-dimensional KDE and its cumulative distribution.\\n        const distribution = oneD(data, bwFactor);\\n        switch (figureName) {\\n            case 'cumulative':\\n                distribution.y = scaleToOne(cumulativeSum(distribution.y));\\n                break;\\n            default:\\n                break;\\n        }\\n        // Compute the point statistics for the given data.\\n        const stats = computeStats(data, distribution.x, distribution.y, hdiProbability);\\n        output[figureName] = {\\n            distribution: distribution,\\n            hdi: highestDensityInterval_data(data, distribution.x, distribution.y, hdiProbability),\\n            stats: { x: stats.x, y: stats.y, text: stats.text },\\n            labels: stats,\\n        };\\n    }\\n    return output;\\n};\\n/**\\n * Callback used to update the Bokeh application in the notebook.\\n *\\n * @param {number[]} data - Raw random variable data from the model.\\n * @param {string} rvName - The name of the random variable from the model.\\n * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when\\n *     calculating the kernel density estimate.\\n * @param {number} hdiProbability - The highest density interval probability to use when\\n *     calculating the HDI.\\n * @param {interfaces.Sources} sources - Bokeh sources used to render glyphs in the\\n *     application.\\n * @param {interfaces.Figures} figures - Bokeh figures shown in the application.\\n * @param {interfaces.Tooltips} tooltips - Bokeh tooltips shown on the glyphs.\\n * @returns {number} We display the value of the bandwidth used for computing the Kernel\\n *     Density Estimate in a div, and must return that value here in order to update the\\n *     value displayed to the user.\\n */\\nconst update = (data, rvName, bwFactor, hdiProbability, sources, figures, tooltips) =&gt; {\\n    const computedData = computeData(data, bwFactor, hdiProbability);\\n    for (let i = 0; i &lt; figureNames.length; i += 1) {\\n        // Update all sources with new data calculated above.\\n        const figureName = figureNames[i];\\n        sources[figureName].distribution.data = {\\n            x: computedData[figureName].distribution.x,\\n            y: computedData[figureName].distribution.y,\\n        };\\n        sources[figureName].hdi.data = {\\n            base: computedData[figureName].hdi.base,\\n            lower: computedData[figureName].hdi.lower,\\n            upper: computedData[figureName].hdi.upper,\\n        };\\n        sources[figureName].stats.data = computedData[figureName].stats;\\n        sources[figureName].labels.data = computedData[figureName].labels;\\n        // Update the axes labels.\\n        updateAxisLabel(figures[figureName].below[0], rvName);\\n        // Update the tooltips.\\n        tooltips[figureName].stats.tooltips = [['', '@text']];\\n        tooltips[figureName].distribution.tooltips = [[rvName, '@x']];\\n    }\\n    return computedData.marginal.distribution.bandwidth;\\n};\\n\\n;// CONCATENATED MODULE: ./src/marginal1d/index.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n// The CustomJS methods used by Bokeh require us to make the JavaScript available in the\\n// browser, which is done by defining it below.\\nwindow.marginal1d = callbacks_namespaceObject;\\n\\n/******/ })()\\n;\\n              bw = marginal1d.update(\\n                rvData,\\n                rvName,\\n                bwFactor,\\n                hdiProbability,\\n                sources,\\n                figures,\\n                tooltips,\\n              );\\n            }\\n        ;\\n            widgets.bw_div.text = `Bandwidth: ${bwFactor * bw}`;\\n        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   &lt;h2&gt;\\n      Highest density interval\\n    &lt;/h2&gt;\\n    &lt;p style=\\\"margin-bottom: 10px\\\"&gt;\\n      The highest density interval region is not equal tailed like a typical\\n      equal tailed interval of 2.5%. Thus it will include the mode(s) of the\\n      posterior distribution.\\n    &lt;/p&gt;\\n    &lt;p style=\\\"margin-bottom: 10px\\\"&gt;\\n      There is nothing particularly specific about having a default HDI of 89%.\\n      If fact, the only remarkable thing about defaulting to 89% is that it is\\n      the highest prime number that does not exceed the unstable 95% threshold.\\n      See the link to McElreath's book below for further discussion.\\n    &lt;/p&gt;\\n    &lt;ul&gt;\\n      &lt;li&gt;\\n        McElreath R (2020)\\n        &lt;b&gt;\\n          Statistical Rethinking: A Bayesian Course with Examples in R and Stan\\n          2nd edition.\\n        &lt;/b&gt;\\n        &lt;em&gt;Chapman and Hall/CRC&lt;/em&gt;\\n        &lt;a\\n          href=https://dx.doi.org/10.1201/9780429029608\\n          style=\\\"color: blue\\\"\\n          target=\\\"_blank\\\"\\n        &gt;\\n          doi: 10.1201/9780429029608\\n        &lt;/a&gt;.\\n      &lt;/li&gt;\\n    &lt;/ul&gt;\\n  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           console.log(toolView);\\n            const bwFactor = 1.0;\\n            const hdiProbability = 0.89;\\n            widgets.bw_factor_slider.value = bwFactor;\\n            widgets.hdi_slider.value = 100 * hdiProbability;\\n            \\n            const rvName = widgets.rv_select.value;\\n            const rvData = data[rvName].flat();\\n            let bw = 0.0;\\n            // Remove the CSS classes that dim the tool output on initial load.\\n            const toolTab = toolView.tabs[0];\\n            const toolChildren = toolTab.child.children;\\n            const dimmedComponent = toolChildren[1];\\n            dimmedComponent.css_classes = [];\\n            try {\\n              bw = marginal1d.update(\\n                rvData,\\n                rvName,\\n                bwFactor,\\n                hdiProbability,\\n                sources,\\n                figures,\\n                tooltips,\\n              );\\n            } catch (error) {\\n              /******/ (() =&gt; { // webpackBootstrap\\n/******/ \\t\\\"use strict\\\";\\n/******/ \\t// The require scope\\n/******/ \\tvar __webpack_require__ = {};\\n/******/ \\t\\n/************************************************************************/\\n/******/ \\t/* webpack/runtime/define property getters */\\n/******/ \\t(() =&gt; {\\n/******/ \\t\\t// define getter functions for harmony exports\\n/******/ \\t\\t__webpack_require__.d = (exports, definition) =&gt; {\\n/******/ \\t\\t\\tfor(var key in definition) {\\n/******/ \\t\\t\\t\\tif(__webpack_require__.o(definition, key) &amp;&amp; !__webpack_require__.o(exports, key)) {\\n/******/ \\t\\t\\t\\t\\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\\n/******/ \\t\\t\\t\\t}\\n/******/ \\t\\t\\t}\\n/******/ \\t\\t};\\n/******/ \\t})();\\n/******/ \\t\\n/******/ \\t/* webpack/runtime/hasOwnProperty shorthand */\\n/******/ \\t(() =&gt; {\\n/******/ \\t\\t__webpack_require__.o = (obj, prop) =&gt; (Object.prototype.hasOwnProperty.call(obj, prop))\\n/******/ \\t})();\\n/******/ \\t\\n/******/ \\t/* webpack/runtime/make namespace object */\\n/******/ \\t(() =&gt; {\\n/******/ \\t\\t// define __esModule on exports\\n/******/ \\t\\t__webpack_require__.r = (exports) =&gt; {\\n/******/ \\t\\t\\tif(typeof Symbol !== 'undefined' &amp;&amp; Symbol.toStringTag) {\\n/******/ \\t\\t\\t\\tObject.defineProperty(exports, Symbol.toStringTag, { value: 'Module' });\\n/******/ \\t\\t\\t}\\n/******/ \\t\\t\\tObject.defineProperty(exports, '__esModule', { value: true });\\n/******/ \\t\\t};\\n/******/ \\t})();\\n/******/ \\t\\n/************************************************************************/\\nvar __webpack_exports__ = {};\\n\\n// NAMESPACE OBJECT: ./src/marginal1d/callbacks.ts\\nvar callbacks_namespaceObject = {};\\n__webpack_require__.r(callbacks_namespaceObject);\\n__webpack_require__.d(callbacks_namespaceObject, {\\n  \\\"computeData\\\": () =&gt; (computeData),\\n  \\\"computeStats\\\": () =&gt; (computeStats),\\n  \\\"update\\\": () =&gt; (update),\\n  \\\"updateAxisLabel\\\": () =&gt; (updateAxisLabel)\\n});\\n\\n;// CONCATENATED MODULE: ./src/stats/array.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n/**\\n * Cumulative sum of the given data.\\n *\\n * @param {number[]} data - Any array of data.\\n * @returns {number[]} The cumulative sum of the given data.\\n */\\nconst cumulativeSum = (data) =&gt; {\\n    // eslint-disable-next-line arrow-body-style\\n    const cumulativeSumMap = ((sum) =&gt; (value) =&gt; {\\n        // eslint-disable-next-line no-return-assign\\n        return (sum += value);\\n    })(0);\\n    const cSum = data.map(cumulativeSumMap);\\n    const normalizationFactor = Math.max(...cSum);\\n    const cumSum = [];\\n    for (let i = 0; i &lt; cSum.length; i += 1) {\\n        cumSum.push(cSum[i] / normalizationFactor);\\n    }\\n    return cumSum;\\n};\\n/**\\n * Numerically sort the given array of numbers.\\n *\\n * @param {number[]} data - The array of numbers to sort.\\n * @returns {number[]} The sorted array of numbers.\\n */\\nconst numericalSort = (data) =&gt; {\\n    const dataCopy = data.slice(0);\\n    return dataCopy.sort((a, b) =&gt; {\\n        return a &lt; b ? -1 : a &gt; b ? 1 : 0;\\n    });\\n};\\n/**\\n * Determine the shape of the given array.\\n *\\n * @param {any[]} data - Any array of data.\\n * @returns {number[]} The shape of the data as an array.\\n */\\nconst shape = (data) =&gt; {\\n    // From https://stackoverflow.com/questions/10237615/get-size-of-dimensions-in-array\\n    const computeShape = (array) =&gt; {\\n        return array.length ? [...[array.length], ...computeShape(array[0])] : [];\\n    };\\n    const arrayShape = computeShape(data);\\n    // Remove the empty array that will exist at the end of the shape array, since it is\\n    // the returned \\\"else\\\" value from above.\\n    const dataShape = [];\\n    for (let i = 0; i &lt; arrayShape.length; i += 1) {\\n        if (!Array.isArray(arrayShape[i])) {\\n            dataShape.push(arrayShape[i]);\\n        }\\n    }\\n    return dataShape;\\n};\\n/**\\n * Create an array that starts and stops with the given number of steps.\\n *\\n * @param {number} start - Where to start the array from.\\n * @param {number} stop - Where to stop the array.\\n * @param {number} [step] - The step size to take.\\n * @param {boolean} [closed] - Flag used to return a closed array or not.\\n * @param {null | number} [size] - If not null, then will return an array with the given\\n *     size.\\n * @returns {number[]} An array that is linearly spaced between the start and stop\\n *     values.\\n */\\nconst linearRange = (start, stop, step = 1, closed = true, size = null) =&gt; {\\n    if (size !== null) {\\n        step = (stop - start) / size;\\n    }\\n    let len = (stop - start) / step + 1;\\n    if (!closed) {\\n        len = (stop - start - step) / step + 1;\\n    }\\n    return Array.from({ length: len }, (_, i) =&gt; {\\n        return start + i * step;\\n    });\\n};\\n/**\\n * Return the indices that would sort the array. Follows NumPy's implementation.\\n *\\n * @param {number[]} data - The data to sort.\\n * @returns {number[]} An array of indices that would sort the original array.\\n */\\nconst array_argSort = (data) =&gt; {\\n    const dataCopy = data.slice(0);\\n    return dataCopy\\n        .map((value, index) =&gt; {\\n        return [value, index];\\n    })\\n        .sort((a, b) =&gt; {\\n        return a[0] - b[0];\\n    })\\n        .map((value) =&gt; {\\n        return value[1];\\n    });\\n};\\n/**\\n * Count the number of time a value appears in an array.\\n *\\n * @param {number[]} data - The numeric array to count objects for.\\n * @returns {{[key: string]: number}} An object that contains the keys as the items in\\n *     the original array, and values that are counts of the key.\\n */\\nconst array_valueCounts = (data) =&gt; {\\n    const counts = {};\\n    for (let i = 0; i &lt; data.length; i += 1) {\\n        counts[data[i]] = (counts[data[i]] || 0) + 1;\\n    }\\n    return counts;\\n};\\n\\n;// CONCATENATED MODULE: ./src/stats/dataTransformation.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n/**\\n * Scale the given array of numbers by the given scaleFactor. Note that this method\\n * divides values in the given array by the scaleFactor.\\n *\\n * @param {number[]} data - An array of numbers that needs to be scaled.\\n * @param {number} scaleFactor - The value to divide all array values by.\\n * @returns {number[]} The scaled array.\\n */\\nconst scaleBy = (data, scaleFactor) =&gt; {\\n    const scaledData = data.map((datum) =&gt; {\\n        return datum / scaleFactor;\\n    });\\n    return scaledData;\\n};\\n/**\\n * Scale the given array to a maximum value of 1. This method uses the scaleBy method,\\n * and finds the maximum value of the given array to use as the scaleFactor of the\\n * scaleBy method.\\n *\\n * @param {number[]} data - Array of data to scale to one.\\n * @returns {number[]} The data scaled to one.\\n */\\nconst scaleToOne = (data) =&gt; {\\n    const scaleFactor = Math.max(...data);\\n    return scaleBy(data, scaleFactor);\\n};\\n/**\\n * Assign ranks to the given data. Follows SciPy's and ArviZ's implementations.\\n *\\n * @param {number[]} data - The numeric data to rank.\\n * @returns {number[]} An array of rankings.\\n */\\nconst rankData = (data) =&gt; {\\n    const n = data.length;\\n    const rank = Array(n);\\n    const sortedIndex = argSort(data);\\n    for (let i = 0; i &lt; rank.length; i += 1) {\\n        rank[sortedIndex[i]] = i + 1;\\n    }\\n    const counts = valueCounts(data);\\n    const countsArray = Object.entries(counts);\\n    const keys = [];\\n    const keyCounts = [];\\n    for (let i = 0; i &lt; countsArray.length; i += 1) {\\n        const [key, count] = countsArray[i];\\n        if (count &gt; 1) {\\n            keys.push(parseFloat(key));\\n            keyCounts.push(count);\\n        }\\n    }\\n    for (let i = 0; i &lt; keys.length; i += 1) {\\n        const repeatIndices = [];\\n        for (let j = 0; j &lt; data.length; j += 1) {\\n            if (data[j] === keys[i]) {\\n                repeatIndices.push(j);\\n            }\\n        }\\n        const rankValues = [];\\n        for (let k = 0; k &lt; repeatIndices.length; k += 1) {\\n            rankValues.push(rank[repeatIndices[k]]);\\n        }\\n        const sum = rankValues.reduce((previousValue, currentValue) =&gt; {\\n            return previousValue + currentValue;\\n        }, 0.0);\\n        const rankMean = sum / rankValues.length;\\n        for (let k = 0; k &lt; repeatIndices.length; k += 1) {\\n            rank[repeatIndices[k]] = rankMean;\\n        }\\n    }\\n    return rank;\\n};\\n\\n;// CONCATENATED MODULE: ./src/stats/highestDensityInterval.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n/**\\n * Find the lower and upper bound of the Highest Density Interval (HDI) for the given\\n * data.\\n *\\n * @param {number[]} data - Raw random variable data from the model.\\n * @param {number} hdiProbability - The highest density interval probability to use when\\n *     calculating the HDI.\\n * @returns {HdiInterval} An object defining the lower and upper bound for the HDI of\\n *     the given data, as well as the indices of the lower and upper bound for the\\n *     sorted raw data.\\n */\\nconst interval = (data, hdiProbability) =&gt; {\\n    const N = data.length;\\n    const sortedData = numericalSort(data);\\n    const stopIndex = Math.floor(hdiProbability * N);\\n    const startIndex = N - stopIndex;\\n    const leftData = sortedData.slice(stopIndex);\\n    const rightData = sortedData.slice(0, startIndex);\\n    const hdi = [];\\n    for (let i = 0; i &lt; leftData.length; i += 1) {\\n        hdi.push(leftData[i] - rightData[i]);\\n    }\\n    const lowerIndex = hdi.indexOf(Math.min(...hdi));\\n    const upperIndex = lowerIndex + stopIndex;\\n    return {\\n        lowerBound: sortedData[lowerIndex],\\n        upperBound: sortedData[upperIndex],\\n        lowerBoundIndex: lowerIndex,\\n        upperBoundIndex: upperIndex,\\n    };\\n};\\n/**\\n * Constructs x and y arrays from the HDI bounds using the raw random variable data. It\\n * also returns the actual HDI bounds of the random variable data.\\n *\\n * @param {number[]} rvData - Raw random variable data from the model.\\n * @param {number[]} marginalX - The support of the Kernel Density Estimate of the\\n *     random variable.\\n * @param {number[]} marginalY - The Kernel Density Estimate of the random variable.\\n * @param {number} hdiProbability - The highest density interval probability to use when\\n *     calculating the HDI.\\n * @returns {HdiData} The x array for the HDI data is the \\\"lower\\\" key, while the y array\\n *     for the HDI data is the \\\"upper\\\" key and the \\\"base\\\" key is the support along the\\n *     axis the HDI is calculated. This nomenclature mirrors the Bokeh Band object,\\n *     which is the annotation type we will use when rendering the HDI intervals.\\n */\\nconst highestDensityInterval_data = (rvData, marginalX, marginalY, hdiProbability) =&gt; {\\n    const hdi = interval(rvData, hdiProbability);\\n    const base = [];\\n    const upper = [];\\n    for (let i = 0; i &lt; marginalX.length; i += 1) {\\n        if (marginalX[i] &lt;= hdi.upperBound &amp;&amp; marginalX[i] &gt;= hdi.lowerBound) {\\n            base.push(marginalX[i]);\\n            upper.push(marginalY[i]);\\n        }\\n    }\\n    // NOTE: We mirror the Bokeh Band annotation object key names below. The lower key is\\n    //       an array of zeros since we will fill the Band all the way to the lower axis.\\n    return {\\n        base: base,\\n        lower: new Array(base.length).fill(0.0),\\n        upper: upper,\\n        lowerBound: hdi.lowerBound,\\n        upperBound: hdi.upperBound,\\n    };\\n};\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/accessor.js\\nfunction accessor(x, fallback) {\\n  return x == null ? fallback\\n    : typeof x === 'function' ? x\\n    : d =&gt; d[x];\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/bin1d.js\\nfunction bin1d(data, x, weight, lo, hi, n) {\\n  const grid = new Float64Array(n);\\n  const delta = (n - 1) / (hi - lo);\\n\\n  for (let i = 0; i &lt; data.length; ++i) {\\n    const d = data[i];\\n    const xi = x(d, i, data);\\n    const wi = weight(d, i, data);\\n\\n    // skip NaN and Infinite values\\n    if (!(Number.isFinite(xi) &amp;&amp; Number.isFinite(wi))) {\\n      continue;\\n    }\\n\\n    const p = (xi - lo) * delta;\\n    const u = Math.floor(p);\\n    const v = u + 1;\\n\\n    if (0 &lt;= u &amp;&amp; v &lt; n) {\\n      grid[u] += (v - p) * wi;\\n      grid[v] += (p - u) * wi;\\n    } else if (u === -1) {\\n      grid[v] += (p - u) * wi;\\n    } else if (v === n) {\\n      grid[u] += (v - p) * wi;\\n    }\\n  }\\n\\n  return grid;\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/deriche.js\\n// Deriche's approximation of Gaussian smoothing\\n// Adapted from Getreuer's C implementation (BSD license)\\n// https://www.ipol.im/pub/art/2013/87/gaussian_20131215.tgz\\n// http://dev.ipol.im/~getreuer/code/doc/gaussian_20131215_doc/gaussian__conv__deriche_8c.html\\n\\nfunction dericheConfig(sigma, negative = false) {\\n  // compute causal filter coefficients\\n  const a = new Float64Array(5);\\n  const bc = new Float64Array(4);\\n  dericheCausalCoeff(a, bc, sigma);\\n\\n  // numerator coefficients of the anticausal filter\\n  const ba = Float64Array.of(\\n    0,\\n    bc[1] - a[1] * bc[0],\\n    bc[2] - a[2] * bc[0],\\n    bc[3] - a[3] * bc[0],\\n    -a[4] * bc[0]\\n  );\\n\\n  // impulse response sums\\n  const accum_denom = 1.0 + a[1] + a[2] + a[3] + a[4];\\n  const sum_causal = (bc[0] + bc[1] + bc[2] + bc[3]) / accum_denom;\\n  const sum_anticausal = (ba[1] + ba[2] + ba[3] + ba[4]) / accum_denom;\\n\\n  // coefficients object\\n  return {\\n    sigma,\\n    negative,\\n    a,\\n    b_causal: bc,\\n    b_anticausal: ba,\\n    sum_causal,\\n    sum_anticausal\\n  };\\n}\\n\\nfunction dericheCausalCoeff(a_out, b_out, sigma) {\\n  const K = 4;\\n\\n  const alpha = Float64Array.of(\\n    0.84, 1.8675,\\n    0.84, -1.8675,\\n    -0.34015, -0.1299,\\n    -0.34015, 0.1299\\n  );\\n\\n  const x1 = Math.exp(-1.783 / sigma);\\n  const x2 = Math.exp(-1.723 / sigma);\\n  const y1 = 0.6318 / sigma;\\n  const y2 = 1.997 / sigma;\\n  const beta = Float64Array.of(\\n    -x1 * Math.cos( y1), x1 * Math.sin( y1),\\n    -x1 * Math.cos(-y1), x1 * Math.sin(-y1),\\n    -x2 * Math.cos( y2), x2 * Math.sin( y2),\\n    -x2 * Math.cos(-y2), x2 * Math.sin(-y2)\\n  );\\n\\n  const denom = sigma * 2.5066282746310007;\\n\\n  // initialize b/a = alpha[0] / (1 + beta[0] z^-1)\\n  const b = Float64Array.of(alpha[0], alpha[1], 0, 0, 0, 0, 0, 0);\\n  const a = Float64Array.of(1, 0, beta[0], beta[1], 0, 0, 0, 0, 0, 0);\\n\\n  let j, k;\\n\\n  for (k = 2; k &lt; 8; k += 2) {\\n    // add kth term, b/a += alpha[k] / (1 + beta[k] z^-1)\\n    b[k]     = beta[k] * b[k - 2] - beta[k + 1] * b[k - 1];\\n    b[k + 1] = beta[k] * b[k - 1] + beta[k + 1] * b[k - 2];\\n    for (j = k - 2; j &gt; 0; j -= 2) {\\n      b[j]     += beta[k] * b[j - 2] - beta[k + 1] * b[j - 1];\\n      b[j + 1] += beta[k] * b[j - 1] + beta[k + 1] * b[j - 2];\\n    }\\n    for (j = 0; j &lt;= k; j += 2) {\\n      b[j]     += alpha[k] * a[j]     - alpha[k + 1] * a[j + 1];\\n      b[j + 1] += alpha[k] * a[j + 1] + alpha[k + 1] * a[j];\\n    }\\n\\n    a[k + 2] = beta[k] * a[k]     - beta[k + 1] * a[k + 1];\\n    a[k + 3] = beta[k] * a[k + 1] + beta[k + 1] * a[k];\\n    for (j = k; j &gt; 0; j -= 2) {\\n      a[j]     += beta[k] * a[j - 2] - beta[k + 1] * a[j - 1];\\n      a[j + 1] += beta[k] * a[j - 1] + beta[k + 1] * a[j - 2];\\n    }\\n  }\\n\\n  for (k = 0; k &lt; K; ++k) {\\n    j = k &lt;&lt; 1;\\n    b_out[k] = b[j] / denom;\\n    a_out[k + 1] = a[j + 2];\\n  }\\n}\\n\\nfunction dericheConv2d(cx, cy, grid, [nx, ny]) {\\n  // allocate buffers\\n  const yc = new Float64Array(Math.max(nx, ny)); // causal\\n  const ya = new Float64Array(Math.max(nx, ny)); // anticausal\\n  const h = new Float64Array(5);\\n  const d = new Float64Array(grid.length);\\n\\n  // convolve rows\\n  for (let row = 0, r0 = 0; row &lt; ny; ++row, r0 += nx) {\\n    const dx = d.subarray(r0);\\n    dericheConv1d(cx, grid.subarray(r0), nx, 1, yc, ya, h, dx);\\n  }\\n\\n  // convolve columns\\n  for (let c0 = 0; c0 &lt; nx; ++c0) {\\n    const dy = d.subarray(c0);\\n    dericheConv1d(cy, dy, ny, nx, yc, ya, h, dy);\\n  }\\n\\n  return d;\\n}\\n\\nfunction dericheConv1d(\\n  c, src, N,\\n  stride = 1,\\n  y_causal = new Float64Array(N),\\n  y_anticausal = new Float64Array(N),\\n  h = new Float64Array(5),\\n  d = y_causal,\\n  init = dericheInitZeroPad\\n) {\\n  const stride_2 = stride * 2;\\n  const stride_3 = stride * 3;\\n  const stride_4 = stride * 4;\\n  const stride_N = stride * N;\\n  let i, n;\\n\\n  // initialize causal filter on the left boundary\\n  init(\\n    y_causal, src, N, stride,\\n    c.b_causal, 3, c.a, 4, c.sum_causal, h, c.sigma\\n  );\\n\\n  // filter the interior samples using a 4th order filter. Implements:\\n  // for n = K, ..., N - 1,\\n  //   y^+(n) = \\\\sum_{k=0}^{K-1} b^+_k src(n - k)\\n  //          - \\\\sum_{k=1}^K a_k y^+(n - k)\\n  // variable i tracks the offset to the nth sample of src, it is\\n  // updated together with n such that i = stride * n.\\n  for (n = 4, i = stride_4; n &lt; N; ++n, i += stride) {\\n    y_causal[n] = c.b_causal[0] * src[i]\\n      + c.b_causal[1] * src[i - stride]\\n      + c.b_causal[2] * src[i - stride_2]\\n      + c.b_causal[3] * src[i - stride_3]\\n      - c.a[1] * y_causal[n - 1]\\n      - c.a[2] * y_causal[n - 2]\\n      - c.a[3] * y_causal[n - 3]\\n      - c.a[4] * y_causal[n - 4];\\n  }\\n\\n  // initialize the anticausal filter on the right boundary\\n  init(\\n    y_anticausal, src, N, -stride,\\n    c.b_anticausal, 4, c.a, 4, c.sum_anticausal, h, c.sigma\\n  );\\n\\n  // similar to the causal filter above, the following implements:\\n  // for n = K, ..., N - 1,\\n  //   y^-(n) = \\\\sum_{k=1}^K b^-_k src(N - n - 1 - k)\\n  //          - \\\\sum_{k=1}^K a_k y^-(n - k)\\n  // variable i is updated such that i = stride * (N - n - 1).\\n  for (n = 4, i = stride_N - stride * 5; n &lt; N; ++n, i -= stride) {\\n    y_anticausal[n] = c.b_anticausal[1] * src[i + stride]\\n      + c.b_anticausal[2] * src[i + stride_2]\\n      + c.b_anticausal[3] * src[i + stride_3]\\n      + c.b_anticausal[4] * src[i + stride_4]\\n      - c.a[1] * y_anticausal[n - 1]\\n      - c.a[2] * y_anticausal[n - 2]\\n      - c.a[3] * y_anticausal[n - 3]\\n      - c.a[4] * y_anticausal[n - 4];\\n  }\\n\\n  // sum the causal and anticausal responses to obtain the final result\\n  if (c.negative) {\\n    // do not threshold if the input grid includes negatively weighted values\\n    for (n = 0, i = 0; n &lt; N; ++n, i += stride) {\\n      d[i] = y_causal[n] + y_anticausal[N - n - 1];\\n    }\\n  } else {\\n    // threshold to prevent small negative values due to floating point error\\n    for (n = 0, i = 0; n &lt; N; ++n, i += stride) {\\n      d[i] = Math.max(0, y_causal[n] + y_anticausal[N - n - 1]);\\n    }\\n  }\\n\\n  return d;\\n}\\n\\nfunction dericheInitZeroPad(dest, src, N, stride, b, p, a, q, sum, h) {\\n  const stride_N = Math.abs(stride) * N;\\n  const off = stride &lt; 0 ? stride_N + stride : 0;\\n  let i, n, m;\\n\\n  // compute the first q taps of the impulse response, h_0, ..., h_{q-1}\\n  for (n = 0; n &lt; q; ++n) {\\n    h[n] = (n &lt;= p) ? b[n] : 0;\\n    for (m = 1; m &lt;= q &amp;&amp; m &lt;= n; ++m) {\\n      h[n] -= a[m] * h[n - m];\\n    }\\n  }\\n\\n  // compute dest_m = sum_{n=1}^m h_{m-n} src_n, m = 0, ..., q-1\\n  // note: q == 4\\n  for (m = 0; m &lt; q; ++m) {\\n    for (dest[m] = 0, n = 1; n &lt;= m; ++n) {\\n      i = off + stride * n;\\n      if (i &gt;= 0 &amp;&amp; i &lt; stride_N) {\\n        dest[m] += h[m - n] * src[i];\\n      }\\n    }\\n  }\\n\\n  // dest_m = dest_m + h_{n+m} src_{-n}\\n  const cur = src[off];\\n  if (cur &gt; 0) {\\n    for (m = 0; m &lt; q; ++m) {\\n      dest[m] += h[m] * cur;\\n    }\\n  }\\n\\n  return;\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/extent.js\\nfunction extent(data, x, pad = 0) {\\n  const n = data.length;\\n  let lo;\\n  let hi;\\n  for (let i = 0; i &lt; n; ++i) {\\n    const v = x(data[i], i, data);\\n    if (v != null) {\\n      if (lo === undefined) {\\n        if (v &gt;= v) lo = hi = v;\\n      } else {\\n        if (v &lt; lo) lo = v;\\n        if (v &gt; hi) hi = v;\\n      }\\n    }\\n  }\\n  return [lo - pad, hi + pad];\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/nrd.js\\n// Scott, D. W. (1992) Multivariate Density Estimation:\\n// Theory, Practice, and Visualization. Wiley.\\nfunction nrd(data, x) {\\n  const values = data.map(x).filter(v =&gt; v != null &amp;&amp; v &gt;= v);\\n  values.sort((a, b) =&gt; a - b);\\n  const sd = stdev(values);\\n  const q1 = quantile(values, 0.25);\\n  const q3 = quantile(values, 0.75);\\n\\n  const n = values.length,\\n        h = (q3 - q1) / 1.34,\\n        v = Math.min(sd, h) || sd || Math.abs(q1) || 1;\\n\\n  return 1.06 * v * Math.pow(n, -0.2);\\n}\\n\\nfunction stdev(values) {\\n  const n = values.length;\\n  let count = 0;\\n  let delta;\\n  let mean = 0;\\n  let sum = 0;\\n  for (let i = 0; i &lt; n; ++i) {\\n    const value = values[i];\\n    delta = value - mean;\\n    mean += delta / ++count;\\n    sum += delta * (value - mean);\\n  }\\n  return count &gt; 1 ? Math.sqrt(sum / (count - 1)) : NaN;\\n}\\n\\nfunction quantile(values, p) {\\n  const n = values.length;\\n\\n  if (!n) return NaN;\\n  if ((p = +p) &lt;= 0 || n &lt; 2) return values[0];\\n  if (p &gt;= 1) return values[n - 1];\\n\\n  const i = (n - 1) * p;\\n  const i0 = Math.floor(i);\\n  const v0 = values[i0];\\n  return v0 + (values[i0 + 1] - v0) * (i - i0);\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/density1d.js\\n\\n\\n\\n\\n\\n\\nfunction density1d(data, options = {}) {\\n  const { adjust = 1, pad = 3, bins = 512 } = options;\\n  const x = accessor(options.x, x =&gt; x);\\n  const w = accessor(options.weight, () =&gt; 1 / data.length);\\n\\n  let bandwidth = options.bandwidth ?? adjust * nrd(data, x);\\n\\n  const [lo, hi] = options.extent ?? extent(data, x, pad * bandwidth);\\n  const grid = bin1d(data, x, w, lo, hi, bins);\\n  const delta = (hi - lo) / (bins - 1);\\n  const neg = grid.some(v =&gt; v &lt; 0);\\n\\n  let config = dericheConfig(bandwidth / delta, neg);\\n  let result;\\n\\n  function* points(x = 'x', y = 'y') {\\n    const result = estimator.grid();\\n    const scale = 1 / delta;\\n    for (let i = 0; i &lt; bins; ++i) {\\n      yield {\\n        [x]: lo + i * delta,\\n        [y]: result[i] * scale\\n      };\\n    }\\n  }\\n\\n  const estimator = {\\n    [Symbol.iterator]: points,\\n    points,\\n    grid: () =&gt; result || (result = dericheConv1d(config, grid, bins)),\\n    extent: () =&gt; [lo, hi],\\n    bandwidth(_) {\\n      if (arguments.length) {\\n        if (_ !== bandwidth) {\\n          bandwidth = _;\\n          result = null;\\n          config = dericheConfig(bandwidth / delta, neg);\\n        }\\n        return estimator;\\n      } else {\\n        return bandwidth;\\n      }\\n    }\\n  };\\n\\n  return estimator;\\n}\\n\\n;// CONCATENATED MODULE: ./src/stats/marginal.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n\\n/**\\n * Calculate the one-dimensional Kernel Density Estimate. This method uses the \\\"scott\\\"\\n * estimate for the bandwidth used when calculating the marginal. This bandwidth\\n * estimate is the same as is used in the corresponding Python KDE estimate from ArviZ.\\n *\\n * Scott, D (1979). On optimal and data-based histograms. Biometrika. 66 (3): 605\\u2013610.\\n * doi:10.1093/biomet/66.3.605.\\n *\\n * @param {number[]} data - The raw random variable data of the model.\\n * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when\\n *     calculating the Kernel Density Estimate (KDE).\\n * @returns {{x: number[]; y: number[]; bandwidth: number}} The KDE of the given data,\\n *     along with the numerical value of the bandwidth used to calculate the KDE.\\n */\\n// eslint-disable-next-line import/prefer-default-export\\nconst oneD = (data, bwFactor) =&gt; {\\n    // Calculate the 1D KDE.\\n    const kde1d = density1d(data, {\\n        adjust: bwFactor,\\n        pad: 3,\\n        bins: 512,\\n    });\\n    // Extract the data points from the 1D KDE calculation.\\n    const points = Array.from(kde1d);\\n    const x = [];\\n    const y = [];\\n    for (let i = 0; i &lt; points.length; i += 1) {\\n        x.push(points[i].x);\\n        y.push(points[i].y);\\n    }\\n    return {\\n        x: x,\\n        y: scaleToOne(y),\\n        bandwidth: kde1d.bandwidth(),\\n    };\\n};\\n\\n;// CONCATENATED MODULE: ./src/stats/pointStatistic.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n/**\\n * Syntactic sugar for summing an array of numbers.\\n *\\n * @param {number[]} data - The array of data.\\n * @returns {number} The sum of the array of data.\\n */\\nconst sum = (data) =&gt; {\\n    return data.reduce((previousValue, currentValue) =&gt; {\\n        return previousValue + currentValue;\\n    });\\n};\\n/**\\n * Calculate the mean of the given array of data.\\n *\\n * @param {number[]} data - The array of data.\\n * @returns {number} The mean of the given data.\\n */\\nconst pointStatistic_mean = (data) =&gt; {\\n    const dataSum = sum(data);\\n    return dataSum / data.length;\\n};\\n\\n;// CONCATENATED MODULE: ./src/stats/utils.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\nconst left_edge_index = (point, intervals) =&gt; {\\n    const len = intervals.length;\\n    if (point &lt; intervals[0]) {\\n        return -1;\\n    }\\n    if (point &gt; intervals[len - 1]) {\\n        return intervals.length;\\n    }\\n    let leftEdgeIndex = 0;\\n    let rightEdgeIndex = len - 1;\\n    while (rightEdgeIndex - leftEdgeIndex !== 1) {\\n        const indexOfNumberToCompare = leftEdgeIndex + Math.floor((rightEdgeIndex - leftEdgeIndex) / 2);\\n        if (point &gt;= intervals[indexOfNumberToCompare])\\n            leftEdgeIndex = indexOfNumberToCompare;\\n        else\\n            rightEdgeIndex = indexOfNumberToCompare;\\n    }\\n    return leftEdgeIndex;\\n};\\nconst lerp = (x, x0, y0, x1, y1) =&gt; {\\n    const slope = (y1 - y0) / (x1 - x0);\\n    let res = slope * (x - x0) + y0;\\n    if (!Number.isFinite(res)) {\\n        res = slope * (x - x1) + y1;\\n        if (!Number.isFinite(res) &amp;&amp; y0 === y1)\\n            res = y0;\\n    }\\n    return res;\\n};\\nconst interpolate = (points, x, y) =&gt; {\\n    // Implementation ported from np.interp\\n    const n = points.length;\\n    const results = new Array(n);\\n    for (let i = 0; i &lt; n; i += 1) {\\n        const point = points[i];\\n        if (Number.isNaN(point)) {\\n            results[i] = point;\\n            // eslint-disable-next-line no-continue\\n            continue;\\n        }\\n        const index = left_edge_index(point, x);\\n        if (index === -1) {\\n            // eslint-disable-next-line prefer-destructuring\\n            results[i] = y[0];\\n        }\\n        else if (index === x.length) {\\n            results[i] = y[y.length - 1];\\n        }\\n        else if (index === x.length - 1 || x[index] === point) {\\n            results[i] = y[index];\\n        }\\n        else {\\n            const x0 = x[index];\\n            const y0 = y[index];\\n            const x1 = x[index + 1];\\n            const y1 = y[index + 1];\\n            results[i] = lerp(point, x0, y0, x1, y1);\\n        }\\n    }\\n    return results;\\n};\\n// eslint-disable-next-line import/prefer-default-export\\nconst interpolatePoints = ({ x, y, points, }) =&gt; {\\n    const output = [];\\n    for (let i = 0; i &lt; points.length; i += 1) {\\n        const interpolatedPoint = Array.from(interpolate([points[i]], x, y));\\n        output.push(interpolatedPoint[0]);\\n    }\\n    return output;\\n};\\n\\n;// CONCATENATED MODULE: ./src/marginal1d/callbacks.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n\\n\\n\\n\\n\\n// Define the names of the figures used for this Bokeh application.\\nconst figureNames = ['marginal', 'cumulative'];\\n/**\\n * Update the given Bokeh Axis object with the new label string. You must use this\\n * method to update axis strings using TypeScript, otherwise the ts compiler will throw\\n * a type check error.\\n *\\n * @param {Axis} axis - The Bokeh Axis object needing a new label.\\n * @param {string | null} label - The new label for the Bokeh Axis object.\\n */\\nconst updateAxisLabel = (axis, label) =&gt; {\\n    // Type check requirement.\\n    if ('axis_label' in axis) {\\n        axis.axis_label = label;\\n    }\\n};\\n/**\\n * Compute the following statistics for the given random variable data\\n *\\n * - lower bound for the highest density interval calculated from the marginalX;\\n * - mean of the rawData;\\n * - upper bound for the highest density interval calculated from the marginalY.\\n *\\n * @param {number[]} rawData - Raw random variable data from the model.\\n * @param {number[]} marginalX - The support of the Kernel Density Estimate of the\\n *     random variable.\\n * @param {number[]} marginalY - The Kernel Density Estimate of the random variable.\\n * @param {number | null} [hdiProb=null] - The highest density interval probability\\n *     value. If the default value is not overwritten, then the default HDI probability\\n *     is 0.89. See Statistical Rethinking by McElreath for a description as to why this\\n *     value is the default.\\n * @param {string[]} [text_align=['right', 'center', 'left']] - How to align the text\\n *     shown in the figure for the point statistics.\\n * @param {number[]} [x_offset=[-5, 0, 5]] - Offset values for the text along the\\n *     x-axis.\\n * @param {number[]} [y_offset=[0, 10, 0]] - Offset values for the text along the\\n *     y-axis\\n * @returns {interfaces.LabelsData} Object containing the computed stats.\\n */\\nconst computeStats = (rawData, marginalX, marginalY, hdiProb = null, text_align = ['right', 'center', 'left'], x_offset = [-5, 0, 5], y_offset = [0, 10, 0]) =&gt; {\\n    // Set the default value to 0.89 if no default value has been given.\\n    const hdiProbability = hdiProb ?? 0.89;\\n    // Compute the point statistics for the KDE, and create labels to display them in the\\n    // figures.\\n    const mean = pointStatistic_mean(rawData);\\n    const hdiBounds = interval(rawData, hdiProbability);\\n    const x = [hdiBounds.lowerBound, mean, hdiBounds.upperBound];\\n    const y = interpolatePoints({ x: marginalX, y: marginalY, points: x });\\n    const text = [\\n        `Lower HDI: ${hdiBounds.lowerBound.toFixed(3)}`,\\n        `Mean: ${mean.toFixed(3)}`,\\n        `Upper HDI: ${hdiBounds.upperBound.toFixed(3)}`,\\n    ];\\n    return {\\n        x: x,\\n        y: y,\\n        text: text,\\n        text_align: text_align,\\n        x_offset: x_offset,\\n        y_offset: y_offset,\\n    };\\n};\\n/**\\n * Compute data for the one-dimensional marginal diagnostic tool.\\n *\\n * @param {number[]} data - Raw random variable data from the model.\\n * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when\\n *     calculating the Kernel Density Estimate (KDE).\\n * @param {number} hdiProbability - The highest density interval probability to use when\\n *     calculating the HDI.\\n * @returns {interfaces.Data} The marginal distribution and cumulative\\n *     distribution calculated from the given random variable data. Point statistics are\\n *     also calculated.\\n */\\nconst computeData = (data, bwFactor, hdiProbability) =&gt; {\\n    const output = {};\\n    for (let i = 0; i &lt; figureNames.length; i += 1) {\\n        const figureName = figureNames[i];\\n        output[figureName] = {};\\n        // Compute the one-dimensional KDE and its cumulative distribution.\\n        const distribution = oneD(data, bwFactor);\\n        switch (figureName) {\\n            case 'cumulative':\\n                distribution.y = scaleToOne(cumulativeSum(distribution.y));\\n                break;\\n            default:\\n                break;\\n        }\\n        // Compute the point statistics for the given data.\\n        const stats = computeStats(data, distribution.x, distribution.y, hdiProbability);\\n        output[figureName] = {\\n            distribution: distribution,\\n            hdi: highestDensityInterval_data(data, distribution.x, distribution.y, hdiProbability),\\n            stats: { x: stats.x, y: stats.y, text: stats.text },\\n            labels: stats,\\n        };\\n    }\\n    return output;\\n};\\n/**\\n * Callback used to update the Bokeh application in the notebook.\\n *\\n * @param {number[]} data - Raw random variable data from the model.\\n * @param {string} rvName - The name of the random variable from the model.\\n * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when\\n *     calculating the kernel density estimate.\\n * @param {number} hdiProbability - The highest density interval probability to use when\\n *     calculating the HDI.\\n * @param {interfaces.Sources} sources - Bokeh sources used to render glyphs in the\\n *     application.\\n * @param {interfaces.Figures} figures - Bokeh figures shown in the application.\\n * @param {interfaces.Tooltips} tooltips - Bokeh tooltips shown on the glyphs.\\n * @returns {number} We display the value of the bandwidth used for computing the Kernel\\n *     Density Estimate in a div, and must return that value here in order to update the\\n *     value displayed to the user.\\n */\\nconst update = (data, rvName, bwFactor, hdiProbability, sources, figures, tooltips) =&gt; {\\n    const computedData = computeData(data, bwFactor, hdiProbability);\\n    for (let i = 0; i &lt; figureNames.length; i += 1) {\\n        // Update all sources with new data calculated above.\\n        const figureName = figureNames[i];\\n        sources[figureName].distribution.data = {\\n            x: computedData[figureName].distribution.x,\\n            y: computedData[figureName].distribution.y,\\n        };\\n        sources[figureName].hdi.data = {\\n            base: computedData[figureName].hdi.base,\\n            lower: computedData[figureName].hdi.lower,\\n            upper: computedData[figureName].hdi.upper,\\n        };\\n        sources[figureName].stats.data = computedData[figureName].stats;\\n        sources[figureName].labels.data = computedData[figureName].labels;\\n        // Update the axes labels.\\n        updateAxisLabel(figures[figureName].below[0], rvName);\\n        // Update the tooltips.\\n        tooltips[figureName].stats.tooltips = [['', '@text']];\\n        tooltips[figureName].distribution.tooltips = [[rvName, '@x']];\\n    }\\n    return computedData.marginal.distribution.bandwidth;\\n};\\n\\n;// CONCATENATED MODULE: ./src/marginal1d/index.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n// The CustomJS methods used by Bokeh require us to make the JavaScript available in the\\n// browser, which is done by defining it below.\\nwindow.marginal1d = callbacks_namespaceObject;\\n\\n/******/ })()\\n;\\n              bw = marginal1d.update(\\n                rvData,\\n                rvName,\\n                bwFactor,\\n                hdiProbability,\\n                sources,\\n                figures,\\n                tooltips,\\n              );\\n            }\\n        ;\\n            widgets.bw_div.text = `Bandwidth: ${bwFactor * bw}`;\\n            figures.marginal.reset.emit();\\n        \"},\"id\":\"1889\",\"type\":\"CustomJS\"},{\"attributes\":{},\"id\":\"1898\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1799\",\"type\":\"ResetTool\"},{\"attributes\":{\"max_height\":750,\"max_width\":1030,\"sizing_mode\":\"scale_both\",\"tabs\":[{\"id\":\"1887\"},{\"id\":\"1879\"}]},\"id\":\"1888\",\"type\":\"Tabs\"},{\"attributes\":{},\"id\":\"1795\",\"type\":\"PanTool\"},{\"attributes\":{\"child\":{\"id\":\"1878\"},\"name\":\"helpPanel\",\"title\":\"Help\"},\"id\":\"1879\",\"type\":\"Panel\"},{\"attributes\":{\"axis\":{\"id\":\"1791\"},\"coordinates\":null,\"dimension\":1,\"grid_line_alpha\":0.3,\"grid_line_color\":\"grey\",\"grid_line_width\":0.3,\"group\":null,\"ticker\":null},\"id\":\"1794\",\"type\":\"Grid\"},{\"attributes\":{\"children\":[{\"id\":\"1874\"},{\"id\":\"1885\"}],\"max_height\":750,\"max_width\":1030,\"sizing_mode\":\"scale_both\"},\"id\":\"1886\",\"type\":\"Column\"},{\"attributes\":{\"end\":99,\"js_property_callbacks\":{\"change:value\":[{\"id\":\"1890\"}]},\"start\":1,\"title\":\"HDI\",\"value\":89},\"id\":\"1877\",\"type\":\"Slider\"},{\"attributes\":{\"overlay\":{\"id\":\"1801\"}},\"id\":\"1797\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"1821\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1812\",\"type\":\"DataRange1d\"}],\"root_ids\":[\"1888\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.3\"}}\n",
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           const bwFactor = widgets.bw_factor_slider.value;\\n            const hdiProbability = widgets.hdi_slider.value / 100;\\n            \\n            const rvName = widgets.rv_select.value;\\n            const rvData = data[rvName].flat();\\n            let bw = 0.0;\\n            // Remove the CSS classes that dim the tool output on initial load.\\n            const toolTab = toolView.tabs[0];\\n            const toolChildren = toolTab.child.children;\\n            const dimmedComponent = toolChildren[1];\\n            dimmedComponent.css_classes = [];\\n            try {\\n              bw = marginal1d.update(\\n                rvData,\\n                rvName,\\n                bwFactor,\\n                hdiProbability,\\n                sources,\\n                figures,\\n                tooltips,\\n                widgets,\\n              );\\n            } catch (error) {\\n              /******/ (() =&gt; { // webpackBootstrap\\n/******/ \\t\\\"use strict\\\";\\n/******/ \\t// The require scope\\n/******/ \\tvar __webpack_require__ = {};\\n/******/ \\t\\n/************************************************************************/\\n/******/ \\t/* webpack/runtime/define property getters */\\n/******/ \\t(() =&gt; {\\n/******/ \\t\\t// define getter functions for harmony exports\\n/******/ \\t\\t__webpack_require__.d = (exports, definition) =&gt; {\\n/******/ \\t\\t\\tfor(var key in definition) {\\n/******/ \\t\\t\\t\\tif(__webpack_require__.o(definition, key) &amp;&amp; !__webpack_require__.o(exports, key)) {\\n/******/ \\t\\t\\t\\t\\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\\n/******/ \\t\\t\\t\\t}\\n/******/ \\t\\t\\t}\\n/******/ \\t\\t};\\n/******/ \\t})();\\n/******/ \\t\\n/******/ \\t/* webpack/runtime/hasOwnProperty shorthand */\\n/******/ \\t(() =&gt; {\\n/******/ \\t\\t__webpack_require__.o = (obj, prop) =&gt; (Object.prototype.hasOwnProperty.call(obj, prop))\\n/******/ \\t})();\\n/******/ \\t\\n/******/ \\t/* webpack/runtime/make namespace object */\\n/******/ \\t(() =&gt; {\\n/******/ \\t\\t// define __esModule on exports\\n/******/ \\t\\t__webpack_require__.r = (exports) =&gt; {\\n/******/ \\t\\t\\tif(typeof Symbol !== 'undefined' &amp;&amp; Symbol.toStringTag) {\\n/******/ \\t\\t\\t\\tObject.defineProperty(exports, Symbol.toStringTag, { value: 'Module' });\\n/******/ \\t\\t\\t}\\n/******/ \\t\\t\\tObject.defineProperty(exports, '__esModule', { value: true });\\n/******/ \\t\\t};\\n/******/ \\t})();\\n/******/ \\t\\n/************************************************************************/\\nvar __webpack_exports__ = {};\\n\\n// NAMESPACE OBJECT: ./src/marginal1d/callbacks.ts\\nvar callbacks_namespaceObject = {};\\n__webpack_require__.r(callbacks_namespaceObject);\\n__webpack_require__.d(callbacks_namespaceObject, {\\n  \\\"computeData\\\": () =&gt; (computeData),\\n  \\\"computeStats\\\": () =&gt; (computeStats),\\n  \\\"update\\\": () =&gt; (update),\\n  \\\"updateAxisLabel\\\": () =&gt; (updateAxisLabel)\\n});\\n\\n;// CONCATENATED MODULE: ./src/stats/array.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n/**\\n * Syntactic sugar for summing an array of numbers.\\n *\\n * @param {number[]} data - The array of data.\\n * @returns {number} The sum of the array of data.\\n */\\nconst arraySum = (data) =&gt; {\\n    return data.reduce((previousValue, currentValue) =&gt; {\\n        return previousValue + currentValue;\\n    });\\n};\\n/**\\n * Calculate the mean of the given array of data.\\n *\\n * @param {number[]} data - The array of data.\\n * @returns {number} The mean of the given data.\\n */\\nconst arrayMean = (data) =&gt; {\\n    const dataSum = arraySum(data);\\n    return dataSum / data.length;\\n};\\n/**\\n * Cumulative sum of the given data.\\n *\\n * @param {number[]} data - Any array of data.\\n * @returns {number[]} The cumulative sum of the given data.\\n */\\nconst cumulativeSum = (data) =&gt; {\\n    // eslint-disable-next-line arrow-body-style\\n    const cumulativeSumMap = ((sum) =&gt; (value) =&gt; {\\n        // eslint-disable-next-line no-return-assign\\n        return (sum += value);\\n    })(0);\\n    const cSum = data.map(cumulativeSumMap);\\n    const normalizationFactor = Math.max(...cSum);\\n    const cumSum = [];\\n    for (let i = 0; i &lt; cSum.length; i += 1) {\\n        cumSum.push(cSum[i] / normalizationFactor);\\n    }\\n    return cumSum;\\n};\\n/**\\n * Numerically sort the given array of numbers.\\n *\\n * @param {number[]} data - The array of numbers to sort.\\n * @returns {number[]} The sorted array of numbers.\\n */\\nconst numericalSort = (data) =&gt; {\\n    const dataCopy = data.slice(0);\\n    return dataCopy.sort((a, b) =&gt; {\\n        return a &lt; b ? -1 : a &gt; b ? 1 : 0;\\n    });\\n};\\n/**\\n * Determine the shape of the given array.\\n *\\n * @param {any[]} data - Any array of data.\\n * @returns {number[]} The shape of the data as an array.\\n */\\nconst shape = (data) =&gt; {\\n    // From https://stackoverflow.com/questions/10237615/get-size-of-dimensions-in-array\\n    const computeShape = (array) =&gt; {\\n        return array.length ? [...[array.length], ...computeShape(array[0])] : [];\\n    };\\n    const arrayShape = computeShape(data);\\n    // Remove the empty array that will exist at the end of the shape array, since it is\\n    // the returned \\\"else\\\" value from above.\\n    const dataShape = [];\\n    for (let i = 0; i &lt; arrayShape.length; i += 1) {\\n        if (!Array.isArray(arrayShape[i])) {\\n            dataShape.push(arrayShape[i]);\\n        }\\n    }\\n    return dataShape;\\n};\\n/**\\n * Create an array that starts and stops with the given number of steps.\\n *\\n * @param {number} start - Where to start the array from.\\n * @param {number} stop - Where to stop the array.\\n * @param {number} [step] - The step size to take.\\n * @param {boolean} [closed] - Flag used to return a closed array or not.\\n * @param {null | number} [size] - If not null, then will return an array with the given\\n *     size.\\n * @returns {number[]} An array that is linearly spaced between the start and stop\\n *     values.\\n */\\nconst linearRange = (start, stop, step = 1, closed = true, size = null) =&gt; {\\n    if (size !== null) {\\n        step = (stop - start) / size;\\n    }\\n    let len = (stop - start) / step + 1;\\n    if (!closed) {\\n        len = (stop - start - step) / step + 1;\\n    }\\n    return Array.from({ length: len }, (_, i) =&gt; {\\n        return start + i * step;\\n    });\\n};\\n/**\\n * Return the indices that would sort the array. Follows NumPy's implementation.\\n *\\n * @param {number[]} data - The data to sort.\\n * @returns {number[]} An array of indices that would sort the original array.\\n */\\nconst array_argSort = (data) =&gt; {\\n    const dataCopy = data.slice(0);\\n    return dataCopy\\n        .map((value, index) =&gt; {\\n        return [value, index];\\n    })\\n        .sort((a, b) =&gt; {\\n        return a[0] - b[0];\\n    })\\n        .map((value) =&gt; {\\n        return value[1];\\n    });\\n};\\n/**\\n * Count the number of time a value appears in an array.\\n *\\n * @param {number[]} data - The numeric array to count objects for.\\n * @returns {{[key: string]: number}} An object that contains the keys as the items in\\n *     the original array, and values that are counts of the key.\\n */\\nconst array_valueCounts = (data) =&gt; {\\n    const counts = {};\\n    for (let i = 0; i &lt; data.length; i += 1) {\\n        counts[data[i]] = (counts[data[i]] || 0) + 1;\\n    }\\n    return counts;\\n};\\n/**\\n * Calculate the median value for the given array.\\n *\\n * @param {number[]} data - Numerical array of data.\\n * @returns {number} The median value of the given data.\\n */\\nconst arrayMedian = (data) =&gt; {\\n    const sortedArray = numericalSort(data);\\n    const arrayLength = sortedArray.length;\\n    const isEven = sortedArray.length % 2 === 0;\\n    let median;\\n    if (isEven) {\\n        const index = arrayLength / 2;\\n        median = (sortedArray[index - 1] + sortedArray[index]) / 2;\\n    }\\n    else {\\n        median = sortedArray[Math.floor(arrayLength / 2)];\\n    }\\n    return median;\\n};\\n\\n;// CONCATENATED MODULE: ./src/stats/dataTransformation.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n/**\\n * Scale the given array of numbers by the given scaleFactor. Note that this method\\n * divides values in the given array by the scaleFactor.\\n *\\n * @param {number[]} data - An array of numbers that needs to be scaled.\\n * @param {number} scaleFactor - The value to divide all array values by.\\n * @returns {number[]} The scaled array.\\n */\\nconst scaleBy = (data, scaleFactor) =&gt; {\\n    const scaledData = data.map((datum) =&gt; {\\n        return datum / scaleFactor;\\n    });\\n    return scaledData;\\n};\\n/**\\n * Scale the given array to a maximum value of 1. This method uses the scaleBy method,\\n * and finds the maximum value of the given array to use as the scaleFactor of the\\n * scaleBy method.\\n *\\n * @param {number[]} data - Array of data to scale to one.\\n * @returns {number[]} The data scaled to one.\\n */\\nconst scaleToOne = (data) =&gt; {\\n    const scaleFactor = Math.max(...data);\\n    return scaleBy(data, scaleFactor);\\n};\\n/**\\n * Assign ranks to the given data. Follows SciPy's and ArviZ's implementations.\\n *\\n * @param {number[]} data - The numeric data to rank.\\n * @returns {number[]} An array of rankings.\\n */\\nconst rankData = (data) =&gt; {\\n    const n = data.length;\\n    const rank = Array(n);\\n    const sortedIndex = argSort(data);\\n    for (let i = 0; i &lt; rank.length; i += 1) {\\n        rank[sortedIndex[i]] = i + 1;\\n    }\\n    const counts = valueCounts(data);\\n    const countsArray = Object.entries(counts);\\n    const keys = [];\\n    const keyCounts = [];\\n    for (let i = 0; i &lt; countsArray.length; i += 1) {\\n        const [key, count] = countsArray[i];\\n        if (count &gt; 1) {\\n            keys.push(parseFloat(key));\\n            keyCounts.push(count);\\n        }\\n    }\\n    for (let i = 0; i &lt; keys.length; i += 1) {\\n        const repeatIndices = [];\\n        for (let j = 0; j &lt; data.length; j += 1) {\\n            if (data[j] === keys[i]) {\\n                repeatIndices.push(j);\\n            }\\n        }\\n        const rankValues = [];\\n        for (let k = 0; k &lt; repeatIndices.length; k += 1) {\\n            rankValues.push(rank[repeatIndices[k]]);\\n        }\\n        const sum = rankValues.reduce((previousValue, currentValue) =&gt; {\\n            return previousValue + currentValue;\\n        }, 0.0);\\n        const rankMean = sum / rankValues.length;\\n        for (let k = 0; k &lt; repeatIndices.length; k += 1) {\\n            rank[repeatIndices[k]] = rankMean;\\n        }\\n    }\\n    return rank;\\n};\\n\\n;// CONCATENATED MODULE: ./src/stats/highestDensityInterval.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n/**\\n * Find the lower and upper bound of the Highest Density Interval (HDI) for the given\\n * data.\\n *\\n * @param {number[]} data - Raw random variable data from the model.\\n * @param {number} hdiProbability - The highest density interval probability to use when\\n *     calculating the HDI.\\n * @returns {HdiInterval} An object defining the lower and upper bound for the HDI of\\n *     the given data, as well as the indices of the lower and upper bound for the\\n *     sorted raw data.\\n */\\nconst interval = (data, hdiProbability) =&gt; {\\n    const N = data.length;\\n    const sortedData = numericalSort(data);\\n    const stopIndex = Math.floor(hdiProbability * N);\\n    const startIndex = N - stopIndex;\\n    const leftData = sortedData.slice(stopIndex);\\n    const rightData = sortedData.slice(0, startIndex);\\n    const hdi = [];\\n    for (let i = 0; i &lt; leftData.length; i += 1) {\\n        hdi.push(leftData[i] - rightData[i]);\\n    }\\n    const lowerIndex = hdi.indexOf(Math.min(...hdi));\\n    const upperIndex = lowerIndex + stopIndex;\\n    return {\\n        lowerBound: sortedData[lowerIndex],\\n        upperBound: sortedData[upperIndex],\\n        lowerBoundIndex: lowerIndex,\\n        upperBoundIndex: upperIndex,\\n    };\\n};\\n/**\\n * Constructs x and y arrays from the HDI bounds using the raw random variable data. It\\n * also returns the actual HDI bounds of the random variable data.\\n *\\n * @param {number[]} rvData - Raw random variable data from the model.\\n * @param {number[]} marginalX - The support of the Kernel Density Estimate of the\\n *     random variable.\\n * @param {number[]} marginalY - The Kernel Density Estimate of the random variable.\\n * @param {number} hdiProbability - The highest density interval probability to use when\\n *     calculating the HDI.\\n * @returns {HdiData} The x array for the HDI data is the \\\"lower\\\" key, while the y array\\n *     for the HDI data is the \\\"upper\\\" key and the \\\"base\\\" key is the support along the\\n *     axis the HDI is calculated. This nomenclature mirrors the Bokeh Band object,\\n *     which is the annotation type we will use when rendering the HDI intervals.\\n */\\nconst highestDensityInterval_data = (rvData, marginalX, marginalY, hdiProbability) =&gt; {\\n    const hdi = interval(rvData, hdiProbability);\\n    const base = [];\\n    const upper = [];\\n    for (let i = 0; i &lt; marginalX.length; i += 1) {\\n        if (marginalX[i] &lt;= hdi.upperBound &amp;&amp; marginalX[i] &gt;= hdi.lowerBound) {\\n            base.push(marginalX[i]);\\n            upper.push(marginalY[i]);\\n        }\\n    }\\n    // NOTE: We mirror the Bokeh Band annotation object key names below. The lower key is\\n    //       an array of zeros since we will fill the Band all the way to the lower axis.\\n    return {\\n        base: base,\\n        lower: new Array(base.length).fill(0.0),\\n        upper: upper,\\n        lowerBound: hdi.lowerBound,\\n        upperBound: hdi.upperBound,\\n    };\\n};\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/accessor.js\\nfunction accessor(x, fallback) {\\n  return x == null ? fallback\\n    : typeof x === 'function' ? x\\n    : d =&gt; d[x];\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/bin1d.js\\nfunction bin1d(data, x, weight, lo, hi, n) {\\n  const grid = new Float64Array(n);\\n  const delta = (n - 1) / (hi - lo);\\n\\n  for (let i = 0; i &lt; data.length; ++i) {\\n    const d = data[i];\\n    const xi = x(d, i, data);\\n    const wi = weight(d, i, data);\\n\\n    // skip NaN and Infinite values\\n    if (!(Number.isFinite(xi) &amp;&amp; Number.isFinite(wi))) {\\n      continue;\\n    }\\n\\n    const p = (xi - lo) * delta;\\n    const u = Math.floor(p);\\n    const v = u + 1;\\n\\n    if (0 &lt;= u &amp;&amp; v &lt; n) {\\n      grid[u] += (v - p) * wi;\\n      grid[v] += (p - u) * wi;\\n    } else if (u === -1) {\\n      grid[v] += (p - u) * wi;\\n    } else if (v === n) {\\n      grid[u] += (v - p) * wi;\\n    }\\n  }\\n\\n  return grid;\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/deriche.js\\n// Deriche's approximation of Gaussian smoothing\\n// Adapted from Getreuer's C implementation (BSD license)\\n// https://www.ipol.im/pub/art/2013/87/gaussian_20131215.tgz\\n// http://dev.ipol.im/~getreuer/code/doc/gaussian_20131215_doc/gaussian__conv__deriche_8c.html\\n\\nfunction dericheConfig(sigma, negative = false) {\\n  // compute causal filter coefficients\\n  const a = new Float64Array(5);\\n  const bc = new Float64Array(4);\\n  dericheCausalCoeff(a, bc, sigma);\\n\\n  // numerator coefficients of the anticausal filter\\n  const ba = Float64Array.of(\\n    0,\\n    bc[1] - a[1] * bc[0],\\n    bc[2] - a[2] * bc[0],\\n    bc[3] - a[3] * bc[0],\\n    -a[4] * bc[0]\\n  );\\n\\n  // impulse response sums\\n  const accum_denom = 1.0 + a[1] + a[2] + a[3] + a[4];\\n  const sum_causal = (bc[0] + bc[1] + bc[2] + bc[3]) / accum_denom;\\n  const sum_anticausal = (ba[1] + ba[2] + ba[3] + ba[4]) / accum_denom;\\n\\n  // coefficients object\\n  return {\\n    sigma,\\n    negative,\\n    a,\\n    b_causal: bc,\\n    b_anticausal: ba,\\n    sum_causal,\\n    sum_anticausal\\n  };\\n}\\n\\nfunction dericheCausalCoeff(a_out, b_out, sigma) {\\n  const K = 4;\\n\\n  const alpha = Float64Array.of(\\n    0.84, 1.8675,\\n    0.84, -1.8675,\\n    -0.34015, -0.1299,\\n    -0.34015, 0.1299\\n  );\\n\\n  const x1 = Math.exp(-1.783 / sigma);\\n  const x2 = Math.exp(-1.723 / sigma);\\n  const y1 = 0.6318 / sigma;\\n  const y2 = 1.997 / sigma;\\n  const beta = Float64Array.of(\\n    -x1 * Math.cos( y1), x1 * Math.sin( y1),\\n    -x1 * Math.cos(-y1), x1 * Math.sin(-y1),\\n    -x2 * Math.cos( y2), x2 * Math.sin( y2),\\n    -x2 * Math.cos(-y2), x2 * Math.sin(-y2)\\n  );\\n\\n  const denom = sigma * 2.5066282746310007;\\n\\n  // initialize b/a = alpha[0] / (1 + beta[0] z^-1)\\n  const b = Float64Array.of(alpha[0], alpha[1], 0, 0, 0, 0, 0, 0);\\n  const a = Float64Array.of(1, 0, beta[0], beta[1], 0, 0, 0, 0, 0, 0);\\n\\n  let j, k;\\n\\n  for (k = 2; k &lt; 8; k += 2) {\\n    // add kth term, b/a += alpha[k] / (1 + beta[k] z^-1)\\n    b[k]     = beta[k] * b[k - 2] - beta[k + 1] * b[k - 1];\\n    b[k + 1] = beta[k] * b[k - 1] + beta[k + 1] * b[k - 2];\\n    for (j = k - 2; j &gt; 0; j -= 2) {\\n      b[j]     += beta[k] * b[j - 2] - beta[k + 1] * b[j - 1];\\n      b[j + 1] += beta[k] * b[j - 1] + beta[k + 1] * b[j - 2];\\n    }\\n    for (j = 0; j &lt;= k; j += 2) {\\n      b[j]     += alpha[k] * a[j]     - alpha[k + 1] * a[j + 1];\\n      b[j + 1] += alpha[k] * a[j + 1] + alpha[k + 1] * a[j];\\n    }\\n\\n    a[k + 2] = beta[k] * a[k]     - beta[k + 1] * a[k + 1];\\n    a[k + 3] = beta[k] * a[k + 1] + beta[k + 1] * a[k];\\n    for (j = k; j &gt; 0; j -= 2) {\\n      a[j]     += beta[k] * a[j - 2] - beta[k + 1] * a[j - 1];\\n      a[j + 1] += beta[k] * a[j - 1] + beta[k + 1] * a[j - 2];\\n    }\\n  }\\n\\n  for (k = 0; k &lt; K; ++k) {\\n    j = k &lt;&lt; 1;\\n    b_out[k] = b[j] / denom;\\n    a_out[k + 1] = a[j + 2];\\n  }\\n}\\n\\nfunction dericheConv2d(cx, cy, grid, [nx, ny]) {\\n  // allocate buffers\\n  const yc = new Float64Array(Math.max(nx, ny)); // causal\\n  const ya = new Float64Array(Math.max(nx, ny)); // anticausal\\n  const h = new Float64Array(5);\\n  const d = new Float64Array(grid.length);\\n\\n  // convolve rows\\n  for (let row = 0, r0 = 0; row &lt; ny; ++row, r0 += nx) {\\n    const dx = d.subarray(r0);\\n    dericheConv1d(cx, grid.subarray(r0), nx, 1, yc, ya, h, dx);\\n  }\\n\\n  // convolve columns\\n  for (let c0 = 0; c0 &lt; nx; ++c0) {\\n    const dy = d.subarray(c0);\\n    dericheConv1d(cy, dy, ny, nx, yc, ya, h, dy);\\n  }\\n\\n  return d;\\n}\\n\\nfunction dericheConv1d(\\n  c, src, N,\\n  stride = 1,\\n  y_causal = new Float64Array(N),\\n  y_anticausal = new Float64Array(N),\\n  h = new Float64Array(5),\\n  d = y_causal,\\n  init = dericheInitZeroPad\\n) {\\n  const stride_2 = stride * 2;\\n  const stride_3 = stride * 3;\\n  const stride_4 = stride * 4;\\n  const stride_N = stride * N;\\n  let i, n;\\n\\n  // initialize causal filter on the left boundary\\n  init(\\n    y_causal, src, N, stride,\\n    c.b_causal, 3, c.a, 4, c.sum_causal, h, c.sigma\\n  );\\n\\n  // filter the interior samples using a 4th order filter. Implements:\\n  // for n = K, ..., N - 1,\\n  //   y^+(n) = \\\\sum_{k=0}^{K-1} b^+_k src(n - k)\\n  //          - \\\\sum_{k=1}^K a_k y^+(n - k)\\n  // variable i tracks the offset to the nth sample of src, it is\\n  // updated together with n such that i = stride * n.\\n  for (n = 4, i = stride_4; n &lt; N; ++n, i += stride) {\\n    y_causal[n] = c.b_causal[0] * src[i]\\n      + c.b_causal[1] * src[i - stride]\\n      + c.b_causal[2] * src[i - stride_2]\\n      + c.b_causal[3] * src[i - stride_3]\\n      - c.a[1] * y_causal[n - 1]\\n      - c.a[2] * y_causal[n - 2]\\n      - c.a[3] * y_causal[n - 3]\\n      - c.a[4] * y_causal[n - 4];\\n  }\\n\\n  // initialize the anticausal filter on the right boundary\\n  init(\\n    y_anticausal, src, N, -stride,\\n    c.b_anticausal, 4, c.a, 4, c.sum_anticausal, h, c.sigma\\n  );\\n\\n  // similar to the causal filter above, the following implements:\\n  // for n = K, ..., N - 1,\\n  //   y^-(n) = \\\\sum_{k=1}^K b^-_k src(N - n - 1 - k)\\n  //          - \\\\sum_{k=1}^K a_k y^-(n - k)\\n  // variable i is updated such that i = stride * (N - n - 1).\\n  for (n = 4, i = stride_N - stride * 5; n &lt; N; ++n, i -= stride) {\\n    y_anticausal[n] = c.b_anticausal[1] * src[i + stride]\\n      + c.b_anticausal[2] * src[i + stride_2]\\n      + c.b_anticausal[3] * src[i + stride_3]\\n      + c.b_anticausal[4] * src[i + stride_4]\\n      - c.a[1] * y_anticausal[n - 1]\\n      - c.a[2] * y_anticausal[n - 2]\\n      - c.a[3] * y_anticausal[n - 3]\\n      - c.a[4] * y_anticausal[n - 4];\\n  }\\n\\n  // sum the causal and anticausal responses to obtain the final result\\n  if (c.negative) {\\n    // do not threshold if the input grid includes negatively weighted values\\n    for (n = 0, i = 0; n &lt; N; ++n, i += stride) {\\n      d[i] = y_causal[n] + y_anticausal[N - n - 1];\\n    }\\n  } else {\\n    // threshold to prevent small negative values due to floating point error\\n    for (n = 0, i = 0; n &lt; N; ++n, i += stride) {\\n      d[i] = Math.max(0, y_causal[n] + y_anticausal[N - n - 1]);\\n    }\\n  }\\n\\n  return d;\\n}\\n\\nfunction dericheInitZeroPad(dest, src, N, stride, b, p, a, q, sum, h) {\\n  const stride_N = Math.abs(stride) * N;\\n  const off = stride &lt; 0 ? stride_N + stride : 0;\\n  let i, n, m;\\n\\n  // compute the first q taps of the impulse response, h_0, ..., h_{q-1}\\n  for (n = 0; n &lt; q; ++n) {\\n    h[n] = (n &lt;= p) ? b[n] : 0;\\n    for (m = 1; m &lt;= q &amp;&amp; m &lt;= n; ++m) {\\n      h[n] -= a[m] * h[n - m];\\n    }\\n  }\\n\\n  // compute dest_m = sum_{n=1}^m h_{m-n} src_n, m = 0, ..., q-1\\n  // note: q == 4\\n  for (m = 0; m &lt; q; ++m) {\\n    for (dest[m] = 0, n = 1; n &lt;= m; ++n) {\\n      i = off + stride * n;\\n      if (i &gt;= 0 &amp;&amp; i &lt; stride_N) {\\n        dest[m] += h[m - n] * src[i];\\n      }\\n    }\\n  }\\n\\n  // dest_m = dest_m + h_{n+m} src_{-n}\\n  const cur = src[off];\\n  if (cur &gt; 0) {\\n    for (m = 0; m &lt; q; ++m) {\\n      dest[m] += h[m] * cur;\\n    }\\n  }\\n\\n  return;\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/extent.js\\nfunction extent(data, x, pad = 0) {\\n  const n = data.length;\\n  let lo;\\n  let hi;\\n  for (let i = 0; i &lt; n; ++i) {\\n    const v = x(data[i], i, data);\\n    if (v != null) {\\n      if (lo === undefined) {\\n        if (v &gt;= v) lo = hi = v;\\n      } else {\\n        if (v &lt; lo) lo = v;\\n        if (v &gt; hi) hi = v;\\n      }\\n    }\\n  }\\n  return [lo - pad, hi + pad];\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/nrd.js\\n// Scott, D. W. (1992) Multivariate Density Estimation:\\n// Theory, Practice, and Visualization. Wiley.\\nfunction nrd(data, x) {\\n  const values = data.map(x).filter(v =&gt; v != null &amp;&amp; v &gt;= v);\\n  values.sort((a, b) =&gt; a - b);\\n  const sd = stdev(values);\\n  const q1 = quantile(values, 0.25);\\n  const q3 = quantile(values, 0.75);\\n\\n  const n = values.length,\\n        h = (q3 - q1) / 1.34,\\n        v = Math.min(sd, h) || sd || Math.abs(q1) || 1;\\n\\n  return 1.06 * v * Math.pow(n, -0.2);\\n}\\n\\nfunction stdev(values) {\\n  const n = values.length;\\n  let count = 0;\\n  let delta;\\n  let mean = 0;\\n  let sum = 0;\\n  for (let i = 0; i &lt; n; ++i) {\\n    const value = values[i];\\n    delta = value - mean;\\n    mean += delta / ++count;\\n    sum += delta * (value - mean);\\n  }\\n  return count &gt; 1 ? Math.sqrt(sum / (count - 1)) : NaN;\\n}\\n\\nfunction quantile(values, p) {\\n  const n = values.length;\\n\\n  if (!n) return NaN;\\n  if ((p = +p) &lt;= 0 || n &lt; 2) return values[0];\\n  if (p &gt;= 1) return values[n - 1];\\n\\n  const i = (n - 1) * p;\\n  const i0 = Math.floor(i);\\n  const v0 = values[i0];\\n  return v0 + (values[i0 + 1] - v0) * (i - i0);\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/density1d.js\\n\\n\\n\\n\\n\\n\\nfunction density1d(data, options = {}) {\\n  const { adjust = 1, pad = 3, bins = 512 } = options;\\n  const x = accessor(options.x, x =&gt; x);\\n  const w = accessor(options.weight, () =&gt; 1 / data.length);\\n\\n  let bandwidth = options.bandwidth ?? adjust * nrd(data, x);\\n\\n  const [lo, hi] = options.extent ?? extent(data, x, pad * bandwidth);\\n  const grid = bin1d(data, x, w, lo, hi, bins);\\n  const delta = (hi - lo) / (bins - 1);\\n  const neg = grid.some(v =&gt; v &lt; 0);\\n\\n  let config = dericheConfig(bandwidth / delta, neg);\\n  let result;\\n\\n  function* points(x = 'x', y = 'y') {\\n    const result = estimator.grid();\\n    const scale = 1 / delta;\\n    for (let i = 0; i &lt; bins; ++i) {\\n      yield {\\n        [x]: lo + i * delta,\\n        [y]: result[i] * scale\\n      };\\n    }\\n  }\\n\\n  const estimator = {\\n    [Symbol.iterator]: points,\\n    points,\\n    grid: () =&gt; result || (result = dericheConv1d(config, grid, bins)),\\n    extent: () =&gt; [lo, hi],\\n    bandwidth(_) {\\n      if (arguments.length) {\\n        if (_ !== bandwidth) {\\n          bandwidth = _;\\n          result = null;\\n          config = dericheConfig(bandwidth / delta, neg);\\n        }\\n        return estimator;\\n      } else {\\n        return bandwidth;\\n      }\\n    }\\n  };\\n\\n  return estimator;\\n}\\n\\n;// CONCATENATED MODULE: ./src/stats/marginal.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n\\n/**\\n * Calculate the one-dimensional Kernel Density Estimate. This method uses the \\\"scott\\\"\\n * estimate for the bandwidth used when calculating the marginal. This bandwidth\\n * estimate is the same as is used in the corresponding Python KDE estimate from ArviZ.\\n *\\n * Scott, D (1979). On optimal and data-based histograms. Biometrika. 66 (3): 605\\u2013610.\\n * doi:10.1093/biomet/66.3.605.\\n *\\n * @param {number[]} data - The raw random variable data of the model.\\n * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when\\n *     calculating the Kernel Density Estimate (KDE).\\n * @returns {{x: number[]; y: number[]; bandwidth: number}} The KDE of the given data,\\n *     along with the numerical value of the bandwidth used to calculate the KDE.\\n */\\n// eslint-disable-next-line import/prefer-default-export\\nconst oneD = (data, bwFactor) =&gt; {\\n    // Calculate the 1D KDE.\\n    const kde1d = density1d(data, {\\n        adjust: bwFactor,\\n        pad: 3,\\n        bins: 512,\\n    });\\n    // Extract the data points from the 1D KDE calculation.\\n    const points = Array.from(kde1d);\\n    const x = [];\\n    const y = [];\\n    for (let i = 0; i &lt; points.length; i += 1) {\\n        x.push(points[i].x);\\n        y.push(points[i].y);\\n    }\\n    return {\\n        x: x,\\n        y: scaleToOne(y),\\n        bandwidth: kde1d.bandwidth(),\\n    };\\n};\\n\\n;// CONCATENATED MODULE: ./src/stats/utils.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\nconst left_edge_index = (point, intervals) =&gt; {\\n    const len = intervals.length;\\n    if (point &lt; intervals[0]) {\\n        return -1;\\n    }\\n    if (point &gt; intervals[len - 1]) {\\n        return intervals.length;\\n    }\\n    let leftEdgeIndex = 0;\\n    let rightEdgeIndex = len - 1;\\n    while (rightEdgeIndex - leftEdgeIndex !== 1) {\\n        const indexOfNumberToCompare = leftEdgeIndex + Math.floor((rightEdgeIndex - leftEdgeIndex) / 2);\\n        if (point &gt;= intervals[indexOfNumberToCompare])\\n            leftEdgeIndex = indexOfNumberToCompare;\\n        else\\n            rightEdgeIndex = indexOfNumberToCompare;\\n    }\\n    return leftEdgeIndex;\\n};\\nconst lerp = (x, x0, y0, x1, y1) =&gt; {\\n    const slope = (y1 - y0) / (x1 - x0);\\n    let res = slope * (x - x0) + y0;\\n    if (!Number.isFinite(res)) {\\n        res = slope * (x - x1) + y1;\\n        if (!Number.isFinite(res) &amp;&amp; y0 === y1)\\n            res = y0;\\n    }\\n    return res;\\n};\\nconst interpolate = (points, x, y) =&gt; {\\n    // Implementation ported from np.interp\\n    const n = points.length;\\n    const results = new Array(n);\\n    for (let i = 0; i &lt; n; i += 1) {\\n        const point = points[i];\\n        if (Number.isNaN(point)) {\\n            results[i] = point;\\n            // eslint-disable-next-line no-continue\\n            continue;\\n        }\\n        const index = left_edge_index(point, x);\\n        if (index === -1) {\\n            // eslint-disable-next-line prefer-destructuring\\n            results[i] = y[0];\\n        }\\n        else if (index === x.length) {\\n            results[i] = y[y.length - 1];\\n        }\\n        else if (index === x.length - 1 || x[index] === point) {\\n            results[i] = y[index];\\n        }\\n        else {\\n            const x0 = x[index];\\n            const y0 = y[index];\\n            const x1 = x[index + 1];\\n            const y1 = y[index + 1];\\n            results[i] = lerp(point, x0, y0, x1, y1);\\n        }\\n    }\\n    return results;\\n};\\n// eslint-disable-next-line import/prefer-default-export\\nconst interpolatePoints = ({ x, y, points, }) =&gt; {\\n    const output = [];\\n    for (let i = 0; i &lt; points.length; i += 1) {\\n        const interpolatedPoint = Array.from(interpolate([points[i]], x, y));\\n        output.push(interpolatedPoint[0]);\\n    }\\n    return output;\\n};\\n\\n;// CONCATENATED MODULE: ./src/marginal1d/callbacks.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n\\n\\n\\n\\n// Define the names of the figures used for this Bokeh application.\\nconst figureNames = ['marginal', 'cumulative'];\\n/**\\n * Update the given Bokeh Axis object with the new label string. You must use this\\n * method to update axis strings using TypeScript, otherwise the ts compiler will throw\\n * a type check error.\\n *\\n * @param {Axis} axis - The Bokeh Axis object needing a new label.\\n * @param {string | null} label - The new label for the Bokeh Axis object.\\n */\\nconst updateAxisLabel = (axis, label) =&gt; {\\n    // Type check requirement.\\n    if ('axis_label' in axis) {\\n        axis.axis_label = label;\\n    }\\n};\\n/**\\n * Compute the following statistics for the given random variable data\\n *\\n * - lower bound for the highest density interval calculated from the marginalX;\\n * - mean of the rawData;\\n * - upper bound for the highest density interval calculated from the marginalY.\\n *\\n * @param {number[]} rawData - Raw random variable data from the model.\\n * @param {number[]} marginalX - The support of the Kernel Density Estimate of the\\n *     random variable.\\n * @param {number[]} marginalY - The Kernel Density Estimate of the random variable.\\n * @param {number} activeStatistic - The statistic to show in the tool. 0 is the mean\\n *     and 1 is the median.\\n * @param {number | null} [hdiProb=null] - The highest density interval probability\\n *     value. If the default value is not overwritten, then the default HDI probability\\n *     is 0.89. See Statistical Rethinking by McElreath for a description as to why this\\n *     value is the default.\\n * @param {string[]} [text_align=['right', 'center', 'left']] - How to align the text\\n *     shown in the figure for the point statistics.\\n * @param {number[]} [x_offset=[-5, 0, 5]] - Offset values for the text along the\\n *     x-axis.\\n * @param {number[]} [y_offset=[0, 10, 0]] - Offset values for the text along the\\n *     y-axis\\n * @returns {interfaces.LabelsData} Object containing the computed stats.\\n */\\nconst computeStats = (rawData, marginalX, marginalY, activeStatistic, hdiProb = null, text_align = ['right', 'center', 'left'], x_offset = [-5, 0, 5], y_offset = [0, 10, 0]) =&gt; {\\n    // Set the default value to 0.89 if no default value has been given.\\n    const hdiProbability = hdiProb ?? 0.89;\\n    // Compute the point statistics for the KDE, and create labels to display them in the\\n    // figures.\\n    const mean = arrayMean(rawData);\\n    const median = arrayMedian(rawData);\\n    const hdiBounds = interval(rawData, hdiProbability);\\n    let x = [hdiBounds.lowerBound, mean, median, hdiBounds.upperBound];\\n    let y = interpolatePoints({ x: marginalX, y: marginalY, points: x });\\n    let text = [\\n        `Lower HDI: ${hdiBounds.lowerBound.toFixed(3)}`,\\n        `Mean: ${mean.toFixed(3)}`,\\n        `Median: ${median.toFixed(3)}`,\\n        `Upper HDI: ${hdiBounds.upperBound.toFixed(3)}`,\\n    ];\\n    // We will filter the output based on the active statistic from the tool.\\n    let mask = [];\\n    if (activeStatistic === 0) {\\n        mask = [0, 1, 3];\\n    }\\n    else if (activeStatistic === 1) {\\n        mask = [0, 2, 3];\\n    }\\n    x = mask.map((i) =&gt; {\\n        return x[i];\\n    });\\n    y = mask.map((i) =&gt; {\\n        return y[i];\\n    });\\n    text = mask.map((i) =&gt; {\\n        return text[i];\\n    });\\n    const output = {\\n        x: x,\\n        y: y,\\n        text: text,\\n        text_align: text_align,\\n        x_offset: x_offset,\\n        y_offset: y_offset,\\n    };\\n    return output;\\n};\\n/**\\n * Compute data for the one-dimensional marginal diagnostic tool.\\n *\\n * @param {number[]} data - Raw random variable data from the model.\\n * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when\\n *     calculating the Kernel Density Estimate (KDE).\\n * @param {number} hdiProbability - The highest density interval probability to use when\\n *     calculating the HDI.\\n * @param {number} activeStatistic - The statistic to show in the tool. 0 is the mean\\n *     and 1 is the median.\\n * @returns {interfaces.Data} The marginal distribution and cumulative\\n *     distribution calculated from the given random variable data. Point statistics are\\n *     also calculated.\\n */\\nconst computeData = (data, bwFactor, hdiProbability, activeStatistic) =&gt; {\\n    const output = {};\\n    for (let i = 0; i &lt; figureNames.length; i += 1) {\\n        const figureName = figureNames[i];\\n        output[figureName] = {};\\n        // Compute the one-dimensional KDE and its cumulative distribution.\\n        const distribution = oneD(data, bwFactor);\\n        switch (figureName) {\\n            case 'cumulative':\\n                distribution.y = scaleToOne(cumulativeSum(distribution.y));\\n                break;\\n            default:\\n                break;\\n        }\\n        // Compute the point statistics for the given data.\\n        const stats = computeStats(data, distribution.x, distribution.y, activeStatistic, hdiProbability);\\n        output[figureName] = {\\n            distribution: distribution,\\n            hdi: highestDensityInterval_data(data, distribution.x, distribution.y, hdiProbability),\\n            stats: { x: stats.x, y: stats.y, text: stats.text },\\n            labels: stats,\\n        };\\n    }\\n    return output;\\n};\\n/**\\n * Callback used to update the Bokeh application in the notebook.\\n *\\n * @param {number[]} data - Raw random variable data from the model.\\n * @param {string} rvName - The name of the random variable from the model.\\n * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when\\n *     calculating the kernel density estimate.\\n * @param {number} hdiProbability - The highest density interval probability to use when\\n *     calculating the HDI.\\n * @param {interfaces.Sources} sources - Bokeh sources used to render glyphs in the\\n *     application.\\n * @param {interfaces.Figures} figures - Bokeh figures shown in the application.\\n * @param {interfaces.Tooltips} tooltips - Bokeh tooltips shown on the glyphs.\\n * @param {interfaces.Widgets} widgets - Bokeh widget object for the tool.\\n * @returns {number} We display the value of the bandwidth used for computing the Kernel\\n *     Density Estimate in a div, and must return that value here in order to update the\\n *     value displayed to the user.\\n */\\nconst update = (data, rvName, bwFactor, hdiProbability, sources, figures, tooltips, widgets) =&gt; {\\n    const activeStatistic = widgets.stats_button.active;\\n    const computedData = computeData(data, bwFactor, hdiProbability, activeStatistic);\\n    // Marginal figure.\\n    // eslint-disable-next-line prefer-destructuring\\n    const bandwidth = computedData.marginal.distribution.bandwidth;\\n    sources.marginal.distribution.data = {\\n        x: computedData.marginal.distribution.x,\\n        y: computedData.marginal.distribution.y,\\n    };\\n    sources.marginal.hdi.data = {\\n        base: computedData.marginal.hdi.base,\\n        lower: computedData.marginal.hdi.lower,\\n        upper: computedData.marginal.hdi.upper,\\n    };\\n    sources.marginal.stats.data = computedData.marginal.stats;\\n    sources.marginal.labels.data = computedData.marginal.labels;\\n    tooltips.marginal.distribution.tooltips = [[rvName, '@x']];\\n    tooltips.marginal.stats.tooltips = [['', '@text']];\\n    updateAxisLabel(figures.marginal.below[0], rvName);\\n    // Cumulative figure.\\n    sources.cumulative.distribution.data = {\\n        x: computedData.cumulative.distribution.x,\\n        y: computedData.cumulative.distribution.y,\\n    };\\n    sources.cumulative.hdi.data = {\\n        base: computedData.cumulative.hdi.base,\\n        lower: computedData.cumulative.hdi.lower,\\n        upper: computedData.cumulative.hdi.upper,\\n    };\\n    sources.cumulative.stats.data = computedData.cumulative.stats;\\n    sources.cumulative.labels.data = computedData.cumulative.labels;\\n    tooltips.cumulative.distribution.tooltips = [[rvName, '@x']];\\n    tooltips.cumulative.stats.tooltips = [['', '@text']];\\n    updateAxisLabel(figures.cumulative.below[0], rvName);\\n    return bandwidth;\\n};\\n\\n;// CONCATENATED MODULE: ./src/marginal1d/index.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n// The CustomJS methods used by Bokeh require us to make the JavaScript available in the\\n// browser, which is done by defining it below.\\nwindow.marginal1d = callbacks_namespaceObject;\\n\\n/******/ })()\\n;\\n              bw = marginal1d.update(\\n                rvData,\\n                rvName,\\n                bwFactor,\\n                hdiProbability,\\n                sources,\\n                figures,\\n                tooltips,\\n                widgets,\\n              );\\n            }\\n        ;\\n            widgets.bw_div.text = `Bandwidth: ${bwFactor * bw}`;\\n        \"},\"id\":\"1895\",\"type\":\"CustomJS\"},{\"attributes\":{\"callback\":null,\"renderers\":[{\"id\":\"1850\"}],\"tooltips\":[[\"Select a random variable...\",\"@x\"]]},\"id\":\"1866\",\"type\":\"HoverTool\"},{\"attributes\":{\"active\":0,\"js_property_callbacks\":{\"change:active\":[{\"id\":\"1895\"}]},\"labels\":[\"Mean\",\"Median\"]},\"id\":\"1878\",\"type\":\"RadioButtonGroup\"},{\"attributes\":{},\"id\":\"1919\",\"type\":\"AllLabels\"},{\"attributes\":{\"data\":{\"text\":[],\"x\":[],\"y\":[]},\"selected\":{\"id\":\"1901\"},\"selection_policy\":{\"id\":\"1900\"}},\"id\":\"1770\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1812\",\"type\":\"DataRange1d\"},{\"attributes\":{\"end\":2.0,\"js_property_callbacks\":{\"change:value\":[{\"id\":\"1894\"}]},\"start\":0.01,\"step\":0.01,\"title\":\"Bandwidth 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           const bwFactor = widgets.bw_factor_slider.value;\\n            const hdiProbability = widgets.hdi_slider.value / 100;\\n            \\n            const rvName = widgets.rv_select.value;\\n            const rvData = data[rvName].flat();\\n            let bw = 0.0;\\n            // Remove the CSS classes that dim the tool output on initial load.\\n            const toolTab = toolView.tabs[0];\\n            const toolChildren = toolTab.child.children;\\n            const dimmedComponent = toolChildren[1];\\n            dimmedComponent.css_classes = [];\\n            try {\\n              bw = marginal1d.update(\\n                rvData,\\n                rvName,\\n                bwFactor,\\n                hdiProbability,\\n                sources,\\n                figures,\\n                tooltips,\\n                widgets,\\n              );\\n            } catch (error) {\\n              /******/ (() =&gt; { // webpackBootstrap\\n/******/ \\t\\\"use strict\\\";\\n/******/ \\t// The require scope\\n/******/ \\tvar __webpack_require__ = {};\\n/******/ \\t\\n/************************************************************************/\\n/******/ \\t/* webpack/runtime/define property getters */\\n/******/ \\t(() =&gt; {\\n/******/ \\t\\t// define getter functions for harmony exports\\n/******/ \\t\\t__webpack_require__.d = (exports, definition) =&gt; {\\n/******/ \\t\\t\\tfor(var key in definition) {\\n/******/ \\t\\t\\t\\tif(__webpack_require__.o(definition, key) &amp;&amp; !__webpack_require__.o(exports, key)) {\\n/******/ \\t\\t\\t\\t\\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\\n/******/ \\t\\t\\t\\t}\\n/******/ \\t\\t\\t}\\n/******/ \\t\\t};\\n/******/ \\t})();\\n/******/ \\t\\n/******/ \\t/* webpack/runtime/hasOwnProperty shorthand */\\n/******/ \\t(() =&gt; {\\n/******/ \\t\\t__webpack_require__.o = (obj, prop) =&gt; (Object.prototype.hasOwnProperty.call(obj, prop))\\n/******/ \\t})();\\n/******/ \\t\\n/******/ \\t/* webpack/runtime/make namespace object */\\n/******/ \\t(() =&gt; {\\n/******/ \\t\\t// define __esModule on exports\\n/******/ \\t\\t__webpack_require__.r = (exports) =&gt; {\\n/******/ \\t\\t\\tif(typeof Symbol !== 'undefined' &amp;&amp; Symbol.toStringTag) {\\n/******/ \\t\\t\\t\\tObject.defineProperty(exports, Symbol.toStringTag, { value: 'Module' });\\n/******/ \\t\\t\\t}\\n/******/ \\t\\t\\tObject.defineProperty(exports, '__esModule', { value: true });\\n/******/ \\t\\t};\\n/******/ \\t})();\\n/******/ \\t\\n/************************************************************************/\\nvar __webpack_exports__ = {};\\n\\n// NAMESPACE OBJECT: ./src/marginal1d/callbacks.ts\\nvar callbacks_namespaceObject = {};\\n__webpack_require__.r(callbacks_namespaceObject);\\n__webpack_require__.d(callbacks_namespaceObject, {\\n  \\\"computeData\\\": () =&gt; (computeData),\\n  \\\"computeStats\\\": () =&gt; (computeStats),\\n  \\\"update\\\": () =&gt; (update),\\n  \\\"updateAxisLabel\\\": () =&gt; (updateAxisLabel)\\n});\\n\\n;// CONCATENATED MODULE: ./src/stats/array.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n/**\\n * Syntactic sugar for summing an array of numbers.\\n *\\n * @param {number[]} data - The array of data.\\n * @returns {number} The sum of the array of data.\\n */\\nconst arraySum = (data) =&gt; {\\n    return data.reduce((previousValue, currentValue) =&gt; {\\n        return previousValue + currentValue;\\n    });\\n};\\n/**\\n * Calculate the mean of the given array of data.\\n *\\n * @param {number[]} data - The array of data.\\n * @returns {number} The mean of the given data.\\n */\\nconst arrayMean = (data) =&gt; {\\n    const dataSum = arraySum(data);\\n    return dataSum / data.length;\\n};\\n/**\\n * Cumulative sum of the given data.\\n *\\n * @param {number[]} data - Any array of data.\\n * @returns {number[]} The cumulative sum of the given data.\\n */\\nconst cumulativeSum = (data) =&gt; {\\n    // eslint-disable-next-line arrow-body-style\\n    const cumulativeSumMap = ((sum) =&gt; (value) =&gt; {\\n        // eslint-disable-next-line no-return-assign\\n        return (sum += value);\\n    })(0);\\n    const cSum = data.map(cumulativeSumMap);\\n    const normalizationFactor = Math.max(...cSum);\\n    const cumSum = [];\\n    for (let i = 0; i &lt; cSum.length; i += 1) {\\n        cumSum.push(cSum[i] / normalizationFactor);\\n    }\\n    return cumSum;\\n};\\n/**\\n * Numerically sort the given array of numbers.\\n *\\n * @param {number[]} data - The array of numbers to sort.\\n * @returns {number[]} The sorted array of numbers.\\n */\\nconst numericalSort = (data) =&gt; {\\n    const dataCopy = data.slice(0);\\n    return dataCopy.sort((a, b) =&gt; {\\n        return a &lt; b ? -1 : a &gt; b ? 1 : 0;\\n    });\\n};\\n/**\\n * Determine the shape of the given array.\\n *\\n * @param {any[]} data - Any array of data.\\n * @returns {number[]} The shape of the data as an array.\\n */\\nconst shape = (data) =&gt; {\\n    // From https://stackoverflow.com/questions/10237615/get-size-of-dimensions-in-array\\n    const computeShape = (array) =&gt; {\\n        return array.length ? [...[array.length], ...computeShape(array[0])] : [];\\n    };\\n    const arrayShape = computeShape(data);\\n    // Remove the empty array that will exist at the end of the shape array, since it is\\n    // the returned \\\"else\\\" value from above.\\n    const dataShape = [];\\n    for (let i = 0; i &lt; arrayShape.length; i += 1) {\\n        if (!Array.isArray(arrayShape[i])) {\\n            dataShape.push(arrayShape[i]);\\n        }\\n    }\\n    return dataShape;\\n};\\n/**\\n * Create an array that starts and stops with the given number of steps.\\n *\\n * @param {number} start - Where to start the array from.\\n * @param {number} stop - Where to stop the array.\\n * @param {number} [step] - The step size to take.\\n * @param {boolean} [closed] - Flag used to return a closed array or not.\\n * @param {null | number} [size] - If not null, then will return an array with the given\\n *     size.\\n * @returns {number[]} An array that is linearly spaced between the start and stop\\n *     values.\\n */\\nconst linearRange = (start, stop, step = 1, closed = true, size = null) =&gt; {\\n    if (size !== null) {\\n        step = (stop - start) / size;\\n    }\\n    let len = (stop - start) / step + 1;\\n    if (!closed) {\\n        len = (stop - start - step) / step + 1;\\n    }\\n    return Array.from({ length: len }, (_, i) =&gt; {\\n        return start + i * step;\\n    });\\n};\\n/**\\n * Return the indices that would sort the array. Follows NumPy's implementation.\\n *\\n * @param {number[]} data - The data to sort.\\n * @returns {number[]} An array of indices that would sort the original array.\\n */\\nconst array_argSort = (data) =&gt; {\\n    const dataCopy = data.slice(0);\\n    return dataCopy\\n        .map((value, index) =&gt; {\\n        return [value, index];\\n    })\\n        .sort((a, b) =&gt; {\\n        return a[0] - b[0];\\n    })\\n        .map((value) =&gt; {\\n        return value[1];\\n    });\\n};\\n/**\\n * Count the number of time a value appears in an array.\\n *\\n * @param {number[]} data - The numeric array to count objects for.\\n * @returns {{[key: string]: number}} An object that contains the keys as the items in\\n *     the original array, and values that are counts of the key.\\n */\\nconst array_valueCounts = (data) =&gt; {\\n    const counts = {};\\n    for (let i = 0; i &lt; data.length; i += 1) {\\n        counts[data[i]] = (counts[data[i]] || 0) + 1;\\n    }\\n    return counts;\\n};\\n/**\\n * Calculate the median value for the given array.\\n *\\n * @param {number[]} data - Numerical array of data.\\n * @returns {number} The median value of the given data.\\n */\\nconst arrayMedian = (data) =&gt; {\\n    const sortedArray = numericalSort(data);\\n    const arrayLength = sortedArray.length;\\n    const isEven = sortedArray.length % 2 === 0;\\n    let median;\\n    if (isEven) {\\n        const index = arrayLength / 2;\\n        median = (sortedArray[index - 1] + sortedArray[index]) / 2;\\n    }\\n    else {\\n        median = sortedArray[Math.floor(arrayLength / 2)];\\n    }\\n    return median;\\n};\\n\\n;// CONCATENATED MODULE: ./src/stats/dataTransformation.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n/**\\n * Scale the given array of numbers by the given scaleFactor. Note that this method\\n * divides values in the given array by the scaleFactor.\\n *\\n * @param {number[]} data - An array of numbers that needs to be scaled.\\n * @param {number} scaleFactor - The value to divide all array values by.\\n * @returns {number[]} The scaled array.\\n */\\nconst scaleBy = (data, scaleFactor) =&gt; {\\n    const scaledData = data.map((datum) =&gt; {\\n        return datum / scaleFactor;\\n    });\\n    return scaledData;\\n};\\n/**\\n * Scale the given array to a maximum value of 1. This method uses the scaleBy method,\\n * and finds the maximum value of the given array to use as the scaleFactor of the\\n * scaleBy method.\\n *\\n * @param {number[]} data - Array of data to scale to one.\\n * @returns {number[]} The data scaled to one.\\n */\\nconst scaleToOne = (data) =&gt; {\\n    const scaleFactor = Math.max(...data);\\n    return scaleBy(data, scaleFactor);\\n};\\n/**\\n * Assign ranks to the given data. Follows SciPy's and ArviZ's implementations.\\n *\\n * @param {number[]} data - The numeric data to rank.\\n * @returns {number[]} An array of rankings.\\n */\\nconst rankData = (data) =&gt; {\\n    const n = data.length;\\n    const rank = Array(n);\\n    const sortedIndex = argSort(data);\\n    for (let i = 0; i &lt; rank.length; i += 1) {\\n        rank[sortedIndex[i]] = i + 1;\\n    }\\n    const counts = valueCounts(data);\\n    const countsArray = Object.entries(counts);\\n    const keys = [];\\n    const keyCounts = [];\\n    for (let i = 0; i &lt; countsArray.length; i += 1) {\\n        const [key, count] = countsArray[i];\\n        if (count &gt; 1) {\\n            keys.push(parseFloat(key));\\n            keyCounts.push(count);\\n        }\\n    }\\n    for (let i = 0; i &lt; keys.length; i += 1) {\\n        const repeatIndices = [];\\n        for (let j = 0; j &lt; data.length; j += 1) {\\n            if (data[j] === keys[i]) {\\n                repeatIndices.push(j);\\n            }\\n        }\\n        const rankValues = [];\\n        for (let k = 0; k &lt; repeatIndices.length; k += 1) {\\n            rankValues.push(rank[repeatIndices[k]]);\\n        }\\n        const sum = rankValues.reduce((previousValue, currentValue) =&gt; {\\n            return previousValue + currentValue;\\n        }, 0.0);\\n        const rankMean = sum / rankValues.length;\\n        for (let k = 0; k &lt; repeatIndices.length; k += 1) {\\n            rank[repeatIndices[k]] = rankMean;\\n        }\\n    }\\n    return rank;\\n};\\n\\n;// CONCATENATED MODULE: ./src/stats/highestDensityInterval.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n/**\\n * Find the lower and upper bound of the Highest Density Interval (HDI) for the given\\n * data.\\n *\\n * @param {number[]} data - Raw random variable data from the model.\\n * @param {number} hdiProbability - The highest density interval probability to use when\\n *     calculating the HDI.\\n * @returns {HdiInterval} An object defining the lower and upper bound for the HDI of\\n *     the given data, as well as the indices of the lower and upper bound for the\\n *     sorted raw data.\\n */\\nconst interval = (data, hdiProbability) =&gt; {\\n    const N = data.length;\\n    const sortedData = numericalSort(data);\\n    const stopIndex = Math.floor(hdiProbability * N);\\n    const startIndex = N - stopIndex;\\n    const leftData = sortedData.slice(stopIndex);\\n    const rightData = sortedData.slice(0, startIndex);\\n    const hdi = [];\\n    for (let i = 0; i &lt; leftData.length; i += 1) {\\n        hdi.push(leftData[i] - rightData[i]);\\n    }\\n    const lowerIndex = hdi.indexOf(Math.min(...hdi));\\n    const upperIndex = lowerIndex + stopIndex;\\n    return {\\n        lowerBound: sortedData[lowerIndex],\\n        upperBound: sortedData[upperIndex],\\n        lowerBoundIndex: lowerIndex,\\n        upperBoundIndex: upperIndex,\\n    };\\n};\\n/**\\n * Constructs x and y arrays from the HDI bounds using the raw random variable data. It\\n * also returns the actual HDI bounds of the random variable data.\\n *\\n * @param {number[]} rvData - Raw random variable data from the model.\\n * @param {number[]} marginalX - The support of the Kernel Density Estimate of the\\n *     random variable.\\n * @param {number[]} marginalY - The Kernel Density Estimate of the random variable.\\n * @param {number} hdiProbability - The highest density interval probability to use when\\n *     calculating the HDI.\\n * @returns {HdiData} The x array for the HDI data is the \\\"lower\\\" key, while the y array\\n *     for the HDI data is the \\\"upper\\\" key and the \\\"base\\\" key is the support along the\\n *     axis the HDI is calculated. This nomenclature mirrors the Bokeh Band object,\\n *     which is the annotation type we will use when rendering the HDI intervals.\\n */\\nconst highestDensityInterval_data = (rvData, marginalX, marginalY, hdiProbability) =&gt; {\\n    const hdi = interval(rvData, hdiProbability);\\n    const base = [];\\n    const upper = [];\\n    for (let i = 0; i &lt; marginalX.length; i += 1) {\\n        if (marginalX[i] &lt;= hdi.upperBound &amp;&amp; marginalX[i] &gt;= hdi.lowerBound) {\\n            base.push(marginalX[i]);\\n            upper.push(marginalY[i]);\\n        }\\n    }\\n    // NOTE: We mirror the Bokeh Band annotation object key names below. The lower key is\\n    //       an array of zeros since we will fill the Band all the way to the lower axis.\\n    return {\\n        base: base,\\n        lower: new Array(base.length).fill(0.0),\\n        upper: upper,\\n        lowerBound: hdi.lowerBound,\\n        upperBound: hdi.upperBound,\\n    };\\n};\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/accessor.js\\nfunction accessor(x, fallback) {\\n  return x == null ? fallback\\n    : typeof x === 'function' ? x\\n    : d =&gt; d[x];\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/bin1d.js\\nfunction bin1d(data, x, weight, lo, hi, n) {\\n  const grid = new Float64Array(n);\\n  const delta = (n - 1) / (hi - lo);\\n\\n  for (let i = 0; i &lt; data.length; ++i) {\\n    const d = data[i];\\n    const xi = x(d, i, data);\\n    const wi = weight(d, i, data);\\n\\n    // skip NaN and Infinite values\\n    if (!(Number.isFinite(xi) &amp;&amp; Number.isFinite(wi))) {\\n      continue;\\n    }\\n\\n    const p = (xi - lo) * delta;\\n    const u = Math.floor(p);\\n    const v = u + 1;\\n\\n    if (0 &lt;= u &amp;&amp; v &lt; n) {\\n      grid[u] += (v - p) * wi;\\n      grid[v] += (p - u) * wi;\\n    } else if (u === -1) {\\n      grid[v] += (p - u) * wi;\\n    } else if (v === n) {\\n      grid[u] += (v - p) * wi;\\n    }\\n  }\\n\\n  return grid;\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/deriche.js\\n// Deriche's approximation of Gaussian smoothing\\n// Adapted from Getreuer's C implementation (BSD license)\\n// https://www.ipol.im/pub/art/2013/87/gaussian_20131215.tgz\\n// http://dev.ipol.im/~getreuer/code/doc/gaussian_20131215_doc/gaussian__conv__deriche_8c.html\\n\\nfunction dericheConfig(sigma, negative = false) {\\n  // compute causal filter coefficients\\n  const a = new Float64Array(5);\\n  const bc = new Float64Array(4);\\n  dericheCausalCoeff(a, bc, sigma);\\n\\n  // numerator coefficients of the anticausal filter\\n  const ba = Float64Array.of(\\n    0,\\n    bc[1] - a[1] * bc[0],\\n    bc[2] - a[2] * bc[0],\\n    bc[3] - a[3] * bc[0],\\n    -a[4] * bc[0]\\n  );\\n\\n  // impulse response sums\\n  const accum_denom = 1.0 + a[1] + a[2] + a[3] + a[4];\\n  const sum_causal = (bc[0] + bc[1] + bc[2] + bc[3]) / accum_denom;\\n  const sum_anticausal = (ba[1] + ba[2] + ba[3] + ba[4]) / accum_denom;\\n\\n  // coefficients object\\n  return {\\n    sigma,\\n    negative,\\n    a,\\n    b_causal: bc,\\n    b_anticausal: ba,\\n    sum_causal,\\n    sum_anticausal\\n  };\\n}\\n\\nfunction dericheCausalCoeff(a_out, b_out, sigma) {\\n  const K = 4;\\n\\n  const alpha = Float64Array.of(\\n    0.84, 1.8675,\\n    0.84, -1.8675,\\n    -0.34015, -0.1299,\\n    -0.34015, 0.1299\\n  );\\n\\n  const x1 = Math.exp(-1.783 / sigma);\\n  const x2 = Math.exp(-1.723 / sigma);\\n  const y1 = 0.6318 / sigma;\\n  const y2 = 1.997 / sigma;\\n  const beta = Float64Array.of(\\n    -x1 * Math.cos( y1), x1 * Math.sin( y1),\\n    -x1 * Math.cos(-y1), x1 * Math.sin(-y1),\\n    -x2 * Math.cos( y2), x2 * Math.sin( y2),\\n    -x2 * Math.cos(-y2), x2 * Math.sin(-y2)\\n  );\\n\\n  const denom = sigma * 2.5066282746310007;\\n\\n  // initialize b/a = alpha[0] / (1 + beta[0] z^-1)\\n  const b = Float64Array.of(alpha[0], alpha[1], 0, 0, 0, 0, 0, 0);\\n  const a = Float64Array.of(1, 0, beta[0], beta[1], 0, 0, 0, 0, 0, 0);\\n\\n  let j, k;\\n\\n  for (k = 2; k &lt; 8; k += 2) {\\n    // add kth term, b/a += alpha[k] / (1 + beta[k] z^-1)\\n    b[k]     = beta[k] * b[k - 2] - beta[k + 1] * b[k - 1];\\n    b[k + 1] = beta[k] * b[k - 1] + beta[k + 1] * b[k - 2];\\n    for (j = k - 2; j &gt; 0; j -= 2) {\\n      b[j]     += beta[k] * b[j - 2] - beta[k + 1] * b[j - 1];\\n      b[j + 1] += beta[k] * b[j - 1] + beta[k + 1] * b[j - 2];\\n    }\\n    for (j = 0; j &lt;= k; j += 2) {\\n      b[j]     += alpha[k] * a[j]     - alpha[k + 1] * a[j + 1];\\n      b[j + 1] += alpha[k] * a[j + 1] + alpha[k + 1] * a[j];\\n    }\\n\\n    a[k + 2] = beta[k] * a[k]     - beta[k + 1] * a[k + 1];\\n    a[k + 3] = beta[k] * a[k + 1] + beta[k + 1] * a[k];\\n    for (j = k; j &gt; 0; j -= 2) {\\n      a[j]     += beta[k] * a[j - 2] - beta[k + 1] * a[j - 1];\\n      a[j + 1] += beta[k] * a[j - 1] + beta[k + 1] * a[j - 2];\\n    }\\n  }\\n\\n  for (k = 0; k &lt; K; ++k) {\\n    j = k &lt;&lt; 1;\\n    b_out[k] = b[j] / denom;\\n    a_out[k + 1] = a[j + 2];\\n  }\\n}\\n\\nfunction dericheConv2d(cx, cy, grid, [nx, ny]) {\\n  // allocate buffers\\n  const yc = new Float64Array(Math.max(nx, ny)); // causal\\n  const ya = new Float64Array(Math.max(nx, ny)); // anticausal\\n  const h = new Float64Array(5);\\n  const d = new Float64Array(grid.length);\\n\\n  // convolve rows\\n  for (let row = 0, r0 = 0; row &lt; ny; ++row, r0 += nx) {\\n    const dx = d.subarray(r0);\\n    dericheConv1d(cx, grid.subarray(r0), nx, 1, yc, ya, h, dx);\\n  }\\n\\n  // convolve columns\\n  for (let c0 = 0; c0 &lt; nx; ++c0) {\\n    const dy = d.subarray(c0);\\n    dericheConv1d(cy, dy, ny, nx, yc, ya, h, dy);\\n  }\\n\\n  return d;\\n}\\n\\nfunction dericheConv1d(\\n  c, src, N,\\n  stride = 1,\\n  y_causal = new Float64Array(N),\\n  y_anticausal = new Float64Array(N),\\n  h = new Float64Array(5),\\n  d = y_causal,\\n  init = dericheInitZeroPad\\n) {\\n  const stride_2 = stride * 2;\\n  const stride_3 = stride * 3;\\n  const stride_4 = stride * 4;\\n  const stride_N = stride * N;\\n  let i, n;\\n\\n  // initialize causal filter on the left boundary\\n  init(\\n    y_causal, src, N, stride,\\n    c.b_causal, 3, c.a, 4, c.sum_causal, h, c.sigma\\n  );\\n\\n  // filter the interior samples using a 4th order filter. Implements:\\n  // for n = K, ..., N - 1,\\n  //   y^+(n) = \\\\sum_{k=0}^{K-1} b^+_k src(n - k)\\n  //          - \\\\sum_{k=1}^K a_k y^+(n - k)\\n  // variable i tracks the offset to the nth sample of src, it is\\n  // updated together with n such that i = stride * n.\\n  for (n = 4, i = stride_4; n &lt; N; ++n, i += stride) {\\n    y_causal[n] = c.b_causal[0] * src[i]\\n      + c.b_causal[1] * src[i - stride]\\n      + c.b_causal[2] * src[i - stride_2]\\n      + c.b_causal[3] * src[i - stride_3]\\n      - c.a[1] * y_causal[n - 1]\\n      - c.a[2] * y_causal[n - 2]\\n      - c.a[3] * y_causal[n - 3]\\n      - c.a[4] * y_causal[n - 4];\\n  }\\n\\n  // initialize the anticausal filter on the right boundary\\n  init(\\n    y_anticausal, src, N, -stride,\\n    c.b_anticausal, 4, c.a, 4, c.sum_anticausal, h, c.sigma\\n  );\\n\\n  // similar to the causal filter above, the following implements:\\n  // for n = K, ..., N - 1,\\n  //   y^-(n) = \\\\sum_{k=1}^K b^-_k src(N - n - 1 - k)\\n  //          - \\\\sum_{k=1}^K a_k y^-(n - k)\\n  // variable i is updated such that i = stride * (N - n - 1).\\n  for (n = 4, i = stride_N - stride * 5; n &lt; N; ++n, i -= stride) {\\n    y_anticausal[n] = c.b_anticausal[1] * src[i + stride]\\n      + c.b_anticausal[2] * src[i + stride_2]\\n      + c.b_anticausal[3] * src[i + stride_3]\\n      + c.b_anticausal[4] * src[i + stride_4]\\n      - c.a[1] * y_anticausal[n - 1]\\n      - c.a[2] * y_anticausal[n - 2]\\n      - c.a[3] * y_anticausal[n - 3]\\n      - c.a[4] * y_anticausal[n - 4];\\n  }\\n\\n  // sum the causal and anticausal responses to obtain the final result\\n  if (c.negative) {\\n    // do not threshold if the input grid includes negatively weighted values\\n    for (n = 0, i = 0; n &lt; N; ++n, i += stride) {\\n      d[i] = y_causal[n] + y_anticausal[N - n - 1];\\n    }\\n  } else {\\n    // threshold to prevent small negative values due to floating point error\\n    for (n = 0, i = 0; n &lt; N; ++n, i += stride) {\\n      d[i] = Math.max(0, y_causal[n] + y_anticausal[N - n - 1]);\\n    }\\n  }\\n\\n  return d;\\n}\\n\\nfunction dericheInitZeroPad(dest, src, N, stride, b, p, a, q, sum, h) {\\n  const stride_N = Math.abs(stride) * N;\\n  const off = stride &lt; 0 ? stride_N + stride : 0;\\n  let i, n, m;\\n\\n  // compute the first q taps of the impulse response, h_0, ..., h_{q-1}\\n  for (n = 0; n &lt; q; ++n) {\\n    h[n] = (n &lt;= p) ? b[n] : 0;\\n    for (m = 1; m &lt;= q &amp;&amp; m &lt;= n; ++m) {\\n      h[n] -= a[m] * h[n - m];\\n    }\\n  }\\n\\n  // compute dest_m = sum_{n=1}^m h_{m-n} src_n, m = 0, ..., q-1\\n  // note: q == 4\\n  for (m = 0; m &lt; q; ++m) {\\n    for (dest[m] = 0, n = 1; n &lt;= m; ++n) {\\n      i = off + stride * n;\\n      if (i &gt;= 0 &amp;&amp; i &lt; stride_N) {\\n        dest[m] += h[m - n] * src[i];\\n      }\\n    }\\n  }\\n\\n  // dest_m = dest_m + h_{n+m} src_{-n}\\n  const cur = src[off];\\n  if (cur &gt; 0) {\\n    for (m = 0; m &lt; q; ++m) {\\n      dest[m] += h[m] * cur;\\n    }\\n  }\\n\\n  return;\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/extent.js\\nfunction extent(data, x, pad = 0) {\\n  const n = data.length;\\n  let lo;\\n  let hi;\\n  for (let i = 0; i &lt; n; ++i) {\\n    const v = x(data[i], i, data);\\n    if (v != null) {\\n      if (lo === undefined) {\\n        if (v &gt;= v) lo = hi = v;\\n      } else {\\n        if (v &lt; lo) lo = v;\\n        if (v &gt; hi) hi = v;\\n      }\\n    }\\n  }\\n  return [lo - pad, hi + pad];\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/nrd.js\\n// Scott, D. W. (1992) Multivariate Density Estimation:\\n// Theory, Practice, and Visualization. Wiley.\\nfunction nrd(data, x) {\\n  const values = data.map(x).filter(v =&gt; v != null &amp;&amp; v &gt;= v);\\n  values.sort((a, b) =&gt; a - b);\\n  const sd = stdev(values);\\n  const q1 = quantile(values, 0.25);\\n  const q3 = quantile(values, 0.75);\\n\\n  const n = values.length,\\n        h = (q3 - q1) / 1.34,\\n        v = Math.min(sd, h) || sd || Math.abs(q1) || 1;\\n\\n  return 1.06 * v * Math.pow(n, -0.2);\\n}\\n\\nfunction stdev(values) {\\n  const n = values.length;\\n  let count = 0;\\n  let delta;\\n  let mean = 0;\\n  let sum = 0;\\n  for (let i = 0; i &lt; n; ++i) {\\n    const value = values[i];\\n    delta = value - mean;\\n    mean += delta / ++count;\\n    sum += delta * (value - mean);\\n  }\\n  return count &gt; 1 ? Math.sqrt(sum / (count - 1)) : NaN;\\n}\\n\\nfunction quantile(values, p) {\\n  const n = values.length;\\n\\n  if (!n) return NaN;\\n  if ((p = +p) &lt;= 0 || n &lt; 2) return values[0];\\n  if (p &gt;= 1) return values[n - 1];\\n\\n  const i = (n - 1) * p;\\n  const i0 = Math.floor(i);\\n  const v0 = values[i0];\\n  return v0 + (values[i0 + 1] - v0) * (i - i0);\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/density1d.js\\n\\n\\n\\n\\n\\n\\nfunction density1d(data, options = {}) {\\n  const { adjust = 1, pad = 3, bins = 512 } = options;\\n  const x = accessor(options.x, x =&gt; x);\\n  const w = accessor(options.weight, () =&gt; 1 / data.length);\\n\\n  let bandwidth = options.bandwidth ?? adjust * nrd(data, x);\\n\\n  const [lo, hi] = options.extent ?? extent(data, x, pad * bandwidth);\\n  const grid = bin1d(data, x, w, lo, hi, bins);\\n  const delta = (hi - lo) / (bins - 1);\\n  const neg = grid.some(v =&gt; v &lt; 0);\\n\\n  let config = dericheConfig(bandwidth / delta, neg);\\n  let result;\\n\\n  function* points(x = 'x', y = 'y') {\\n    const result = estimator.grid();\\n    const scale = 1 / delta;\\n    for (let i = 0; i &lt; bins; ++i) {\\n      yield {\\n        [x]: lo + i * delta,\\n        [y]: result[i] * scale\\n      };\\n    }\\n  }\\n\\n  const estimator = {\\n    [Symbol.iterator]: points,\\n    points,\\n    grid: () =&gt; result || (result = dericheConv1d(config, grid, bins)),\\n    extent: () =&gt; [lo, hi],\\n    bandwidth(_) {\\n      if (arguments.length) {\\n        if (_ !== bandwidth) {\\n          bandwidth = _;\\n          result = null;\\n          config = dericheConfig(bandwidth / delta, neg);\\n        }\\n        return estimator;\\n      } else {\\n        return bandwidth;\\n      }\\n    }\\n  };\\n\\n  return estimator;\\n}\\n\\n;// CONCATENATED MODULE: ./src/stats/marginal.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n\\n/**\\n * Calculate the one-dimensional Kernel Density Estimate. This method uses the \\\"scott\\\"\\n * estimate for the bandwidth used when calculating the marginal. This bandwidth\\n * estimate is the same as is used in the corresponding Python KDE estimate from ArviZ.\\n *\\n * Scott, D (1979). On optimal and data-based histograms. Biometrika. 66 (3): 605\\u2013610.\\n * doi:10.1093/biomet/66.3.605.\\n *\\n * @param {number[]} data - The raw random variable data of the model.\\n * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when\\n *     calculating the Kernel Density Estimate (KDE).\\n * @returns {{x: number[]; y: number[]; bandwidth: number}} The KDE of the given data,\\n *     along with the numerical value of the bandwidth used to calculate the KDE.\\n */\\n// eslint-disable-next-line import/prefer-default-export\\nconst oneD = (data, bwFactor) =&gt; {\\n    // Calculate the 1D KDE.\\n    const kde1d = density1d(data, {\\n        adjust: bwFactor,\\n        pad: 3,\\n        bins: 512,\\n    });\\n    // Extract the data points from the 1D KDE calculation.\\n    const points = Array.from(kde1d);\\n    const x = [];\\n    const y = [];\\n    for (let i = 0; i &lt; points.length; i += 1) {\\n        x.push(points[i].x);\\n        y.push(points[i].y);\\n    }\\n    return {\\n        x: x,\\n        y: scaleToOne(y),\\n        bandwidth: kde1d.bandwidth(),\\n    };\\n};\\n\\n;// CONCATENATED MODULE: ./src/stats/utils.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\nconst left_edge_index = (point, intervals) =&gt; {\\n    const len = intervals.length;\\n    if (point &lt; intervals[0]) {\\n        return -1;\\n    }\\n    if (point &gt; intervals[len - 1]) {\\n        return intervals.length;\\n    }\\n    let leftEdgeIndex = 0;\\n    let rightEdgeIndex = len - 1;\\n    while (rightEdgeIndex - leftEdgeIndex !== 1) {\\n        const indexOfNumberToCompare = leftEdgeIndex + Math.floor((rightEdgeIndex - leftEdgeIndex) / 2);\\n        if (point &gt;= intervals[indexOfNumberToCompare])\\n            leftEdgeIndex = indexOfNumberToCompare;\\n        else\\n            rightEdgeIndex = indexOfNumberToCompare;\\n    }\\n    return leftEdgeIndex;\\n};\\nconst lerp = (x, x0, y0, x1, y1) =&gt; {\\n    const slope = (y1 - y0) / (x1 - x0);\\n    let res = slope * (x - x0) + y0;\\n    if (!Number.isFinite(res)) {\\n        res = slope * (x - x1) + y1;\\n        if (!Number.isFinite(res) &amp;&amp; y0 === y1)\\n            res = y0;\\n    }\\n    return res;\\n};\\nconst interpolate = (points, x, y) =&gt; {\\n    // Implementation ported from np.interp\\n    const n = points.length;\\n    const results = new Array(n);\\n    for (let i = 0; i &lt; n; i += 1) {\\n        const point = points[i];\\n        if (Number.isNaN(point)) {\\n            results[i] = point;\\n            // eslint-disable-next-line no-continue\\n            continue;\\n        }\\n        const index = left_edge_index(point, x);\\n        if (index === -1) {\\n            // eslint-disable-next-line prefer-destructuring\\n            results[i] = y[0];\\n        }\\n        else if (index === x.length) {\\n            results[i] = y[y.length - 1];\\n        }\\n        else if (index === x.length - 1 || x[index] === point) {\\n            results[i] = y[index];\\n        }\\n        else {\\n            const x0 = x[index];\\n            const y0 = y[index];\\n            const x1 = x[index + 1];\\n            const y1 = y[index + 1];\\n            results[i] = lerp(point, x0, y0, x1, y1);\\n        }\\n    }\\n    return results;\\n};\\n// eslint-disable-next-line import/prefer-default-export\\nconst interpolatePoints = ({ x, y, points, }) =&gt; {\\n    const output = [];\\n    for (let i = 0; i &lt; points.length; i += 1) {\\n        const interpolatedPoint = Array.from(interpolate([points[i]], x, y));\\n        output.push(interpolatedPoint[0]);\\n    }\\n    return output;\\n};\\n\\n;// CONCATENATED MODULE: ./src/marginal1d/callbacks.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n\\n\\n\\n\\n// Define the names of the figures used for this Bokeh application.\\nconst figureNames = ['marginal', 'cumulative'];\\n/**\\n * Update the given Bokeh Axis object with the new label string. You must use this\\n * method to update axis strings using TypeScript, otherwise the ts compiler will throw\\n * a type check error.\\n *\\n * @param {Axis} axis - The Bokeh Axis object needing a new label.\\n * @param {string | null} label - The new label for the Bokeh Axis object.\\n */\\nconst updateAxisLabel = (axis, label) =&gt; {\\n    // Type check requirement.\\n    if ('axis_label' in axis) {\\n        axis.axis_label = label;\\n    }\\n};\\n/**\\n * Compute the following statistics for the given random variable data\\n *\\n * - lower bound for the highest density interval calculated from the marginalX;\\n * - mean of the rawData;\\n * - upper bound for the highest density interval calculated from the marginalY.\\n *\\n * @param {number[]} rawData - Raw random variable data from the model.\\n * @param {number[]} marginalX - The support of the Kernel Density Estimate of the\\n *     random variable.\\n * @param {number[]} marginalY - The Kernel Density Estimate of the random variable.\\n * @param {number} activeStatistic - The statistic to show in the tool. 0 is the mean\\n *     and 1 is the median.\\n * @param {number | null} [hdiProb=null] - The highest density interval probability\\n *     value. If the default value is not overwritten, then the default HDI probability\\n *     is 0.89. See Statistical Rethinking by McElreath for a description as to why this\\n *     value is the default.\\n * @param {string[]} [text_align=['right', 'center', 'left']] - How to align the text\\n *     shown in the figure for the point statistics.\\n * @param {number[]} [x_offset=[-5, 0, 5]] - Offset values for the text along the\\n *     x-axis.\\n * @param {number[]} [y_offset=[0, 10, 0]] - Offset values for the text along the\\n *     y-axis\\n * @returns {interfaces.LabelsData} Object containing the computed stats.\\n */\\nconst computeStats = (rawData, marginalX, marginalY, activeStatistic, hdiProb = null, text_align = ['right', 'center', 'left'], x_offset = [-5, 0, 5], y_offset = [0, 10, 0]) =&gt; {\\n    // Set the default value to 0.89 if no default value has been given.\\n    const hdiProbability = hdiProb ?? 0.89;\\n    // Compute the point statistics for the KDE, and create labels to display them in the\\n    // figures.\\n    const mean = arrayMean(rawData);\\n    const median = arrayMedian(rawData);\\n    const hdiBounds = interval(rawData, hdiProbability);\\n    let x = [hdiBounds.lowerBound, mean, median, hdiBounds.upperBound];\\n    let y = interpolatePoints({ x: marginalX, y: marginalY, points: x });\\n    let text = [\\n        `Lower HDI: ${hdiBounds.lowerBound.toFixed(3)}`,\\n        `Mean: ${mean.toFixed(3)}`,\\n        `Median: ${median.toFixed(3)}`,\\n        `Upper HDI: ${hdiBounds.upperBound.toFixed(3)}`,\\n    ];\\n    // We will filter the output based on the active statistic from the tool.\\n    let mask = [];\\n    if (activeStatistic === 0) {\\n        mask = [0, 1, 3];\\n    }\\n    else if (activeStatistic === 1) {\\n        mask = [0, 2, 3];\\n    }\\n    x = mask.map((i) =&gt; {\\n        return x[i];\\n    });\\n    y = mask.map((i) =&gt; {\\n        return y[i];\\n    });\\n    text = mask.map((i) =&gt; {\\n        return text[i];\\n    });\\n    const output = {\\n        x: x,\\n        y: y,\\n        text: text,\\n        text_align: text_align,\\n        x_offset: x_offset,\\n        y_offset: y_offset,\\n    };\\n    return output;\\n};\\n/**\\n * Compute data for the one-dimensional marginal diagnostic tool.\\n *\\n * @param {number[]} data - Raw random variable data from the model.\\n * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when\\n *     calculating the Kernel Density Estimate (KDE).\\n * @param {number} hdiProbability - The highest density interval probability to use when\\n *     calculating the HDI.\\n * @param {number} activeStatistic - The statistic to show in the tool. 0 is the mean\\n *     and 1 is the median.\\n * @returns {interfaces.Data} The marginal distribution and cumulative\\n *     distribution calculated from the given random variable data. Point statistics are\\n *     also calculated.\\n */\\nconst computeData = (data, bwFactor, hdiProbability, activeStatistic) =&gt; {\\n    const output = {};\\n    for (let i = 0; i &lt; figureNames.length; i += 1) {\\n        const figureName = figureNames[i];\\n        output[figureName] = {};\\n        // Compute the one-dimensional KDE and its cumulative distribution.\\n        const distribution = oneD(data, bwFactor);\\n        switch (figureName) {\\n            case 'cumulative':\\n                distribution.y = scaleToOne(cumulativeSum(distribution.y));\\n                break;\\n            default:\\n                break;\\n        }\\n        // Compute the point statistics for the given data.\\n        const stats = computeStats(data, distribution.x, distribution.y, activeStatistic, hdiProbability);\\n        output[figureName] = {\\n            distribution: distribution,\\n            hdi: highestDensityInterval_data(data, distribution.x, distribution.y, hdiProbability),\\n            stats: { x: stats.x, y: stats.y, text: stats.text },\\n            labels: stats,\\n        };\\n    }\\n    return output;\\n};\\n/**\\n * Callback used to update the Bokeh application in the notebook.\\n *\\n * @param {number[]} data - Raw random variable data from the model.\\n * @param {string} rvName - The name of the random variable from the model.\\n * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when\\n *     calculating the kernel density estimate.\\n * @param {number} hdiProbability - The highest density interval probability to use when\\n *     calculating the HDI.\\n * @param {interfaces.Sources} sources - Bokeh sources used to render glyphs in the\\n *     application.\\n * @param {interfaces.Figures} figures - Bokeh figures shown in the application.\\n * @param {interfaces.Tooltips} tooltips - Bokeh tooltips shown on the glyphs.\\n * @param {interfaces.Widgets} widgets - Bokeh widget object for the tool.\\n * @returns {number} We display the value of the bandwidth used for computing the Kernel\\n *     Density Estimate in a div, and must return that value here in order to update the\\n *     value displayed to the user.\\n */\\nconst update = (data, rvName, bwFactor, hdiProbability, sources, figures, tooltips, widgets) =&gt; {\\n    const activeStatistic = widgets.stats_button.active;\\n    const computedData = computeData(data, bwFactor, hdiProbability, activeStatistic);\\n    // Marginal figure.\\n    // eslint-disable-next-line prefer-destructuring\\n    const bandwidth = computedData.marginal.distribution.bandwidth;\\n    sources.marginal.distribution.data = {\\n        x: computedData.marginal.distribution.x,\\n        y: computedData.marginal.distribution.y,\\n    };\\n    sources.marginal.hdi.data = {\\n        base: computedData.marginal.hdi.base,\\n        lower: computedData.marginal.hdi.lower,\\n        upper: computedData.marginal.hdi.upper,\\n    };\\n    sources.marginal.stats.data = computedData.marginal.stats;\\n    sources.marginal.labels.data = computedData.marginal.labels;\\n    tooltips.marginal.distribution.tooltips = [[rvName, '@x']];\\n    tooltips.marginal.stats.tooltips = [['', '@text']];\\n    updateAxisLabel(figures.marginal.below[0], rvName);\\n    // Cumulative figure.\\n    sources.cumulative.distribution.data = {\\n        x: computedData.cumulative.distribution.x,\\n        y: computedData.cumulative.distribution.y,\\n    };\\n    sources.cumulative.hdi.data = {\\n        base: computedData.cumulative.hdi.base,\\n        lower: computedData.cumulative.hdi.lower,\\n        upper: computedData.cumulative.hdi.upper,\\n    };\\n    sources.cumulative.stats.data = computedData.cumulative.stats;\\n    sources.cumulative.labels.data = computedData.cumulative.labels;\\n    tooltips.cumulative.distribution.tooltips = [[rvName, '@x']];\\n    tooltips.cumulative.stats.tooltips = [['', '@text']];\\n    updateAxisLabel(figures.cumulative.below[0], rvName);\\n    return bandwidth;\\n};\\n\\n;// CONCATENATED MODULE: ./src/marginal1d/index.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n// The CustomJS methods used by Bokeh require us to make the JavaScript available in the\\n// browser, which is done by defining it below.\\nwindow.marginal1d = callbacks_namespaceObject;\\n\\n/******/ })()\\n;\\n              bw = marginal1d.update(\\n                rvData,\\n                rvName,\\n                bwFactor,\\n                hdiProbability,\\n                sources,\\n                figures,\\n                tooltips,\\n                widgets,\\n              );\\n            }\\n        ;\\n            widgets.bw_div.text = `Bandwidth: ${bwFactor * bw}`;\\n        \"},\"id\":\"1894\",\"type\":\"CustomJS\"},{\"attributes\":{\"tools\":[{\"id\":\"1828\"},{\"id\":\"1829\"},{\"id\":\"1830\"},{\"id\":\"1831\"},{\"id\":\"1832\"},{\"id\":\"1833\"},{\"id\":\"1868\"},{\"id\":\"1869\"}]},\"id\":\"1835\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"1898\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"line_alpha\":0.7,\"line_color\":\"#0072B2\",\"line_width\":2.0,\"name\":\"cumulativeDistributionGlyph\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1846\",\"type\":\"Line\"},{\"attributes\":{\"child\":{\"id\":\"1883\"},\"name\":\"helpPanel\",\"title\":\"Help\"},\"id\":\"1884\",\"type\":\"Panel\"},{\"attributes\":{\"children\":[{\"id\":\"1809\"},{\"id\":\"1877\"}]},\"id\":\"1886\",\"type\":\"Column\"},{\"attributes\":{\"min_width\":500,\"text\":\"\\n    &lt;h2&gt;\\n      Highest density interval\\n    &lt;/h2&gt;\\n    &lt;p style=\\\"margin-bottom: 10px\\\"&gt;\\n      The highest density interval region is not equal tailed like a typical\\n      equal tailed interval of 2.5%. Thus it will include the mode(s) of the\\n      posterior distribution.\\n    &lt;/p&gt;\\n    &lt;p style=\\\"margin-bottom: 10px\\\"&gt;\\n      There is nothing particularly specific about having a default HDI of 89%.\\n      If fact, the only remarkable thing about defaulting to 89% is that it is\\n      the highest prime number that does not exceed the unstable 95% threshold.\\n      See the link to McElreath's book below for further discussion.\\n    &lt;/p&gt;\\n    &lt;ul&gt;\\n      &lt;li&gt;\\n        McElreath R (2020)\\n        &lt;b&gt;\\n          Statistical Rethinking: A Bayesian Course with Examples in R and Stan\\n          2nd edition.\\n        &lt;/b&gt;\\n        &lt;em&gt;Chapman and Hall/CRC&lt;/em&gt;\\n        &lt;a\\n          href=https://dx.doi.org/10.1201/9780429029608\\n          style=\\\"color: blue\\\"\\n          target=\\\"_blank\\\"\\n        &gt;\\n          doi: 10.1201/9780429029608\\n        &lt;/a&gt;.\\n      &lt;/li&gt;\\n    &lt;/ul&gt;\\n    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           const bwFactor = 1.0;\\n            const hdiProbability = 0.89;\\n            widgets.bw_factor_slider.value = bwFactor;\\n            widgets.hdi_slider.value = 100 * hdiProbability;\\n            \\n            const rvName = widgets.rv_select.value;\\n            const rvData = data[rvName].flat();\\n            let bw = 0.0;\\n            // Remove the CSS classes that dim the tool output on initial load.\\n            const toolTab = toolView.tabs[0];\\n            const toolChildren = toolTab.child.children;\\n            const dimmedComponent = toolChildren[1];\\n            dimmedComponent.css_classes = [];\\n            try {\\n              bw = marginal1d.update(\\n                rvData,\\n                rvName,\\n                bwFactor,\\n                hdiProbability,\\n                sources,\\n                figures,\\n                tooltips,\\n                widgets,\\n              );\\n            } catch (error) {\\n              /******/ (() =&gt; { // webpackBootstrap\\n/******/ \\t\\\"use strict\\\";\\n/******/ \\t// The require scope\\n/******/ \\tvar __webpack_require__ = {};\\n/******/ \\t\\n/************************************************************************/\\n/******/ \\t/* webpack/runtime/define property getters */\\n/******/ \\t(() =&gt; {\\n/******/ \\t\\t// define getter functions for harmony exports\\n/******/ \\t\\t__webpack_require__.d = (exports, definition) =&gt; {\\n/******/ \\t\\t\\tfor(var key in definition) {\\n/******/ \\t\\t\\t\\tif(__webpack_require__.o(definition, key) &amp;&amp; !__webpack_require__.o(exports, key)) {\\n/******/ \\t\\t\\t\\t\\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\\n/******/ \\t\\t\\t\\t}\\n/******/ \\t\\t\\t}\\n/******/ \\t\\t};\\n/******/ \\t})();\\n/******/ \\t\\n/******/ \\t/* webpack/runtime/hasOwnProperty shorthand */\\n/******/ \\t(() =&gt; {\\n/******/ \\t\\t__webpack_require__.o = (obj, prop) =&gt; (Object.prototype.hasOwnProperty.call(obj, prop))\\n/******/ \\t})();\\n/******/ \\t\\n/******/ \\t/* webpack/runtime/make namespace object */\\n/******/ \\t(() =&gt; {\\n/******/ \\t\\t// define __esModule on exports\\n/******/ \\t\\t__webpack_require__.r = (exports) =&gt; {\\n/******/ \\t\\t\\tif(typeof Symbol !== 'undefined' &amp;&amp; Symbol.toStringTag) {\\n/******/ \\t\\t\\t\\tObject.defineProperty(exports, Symbol.toStringTag, { value: 'Module' });\\n/******/ \\t\\t\\t}\\n/******/ \\t\\t\\tObject.defineProperty(exports, '__esModule', { value: true });\\n/******/ \\t\\t};\\n/******/ \\t})();\\n/******/ \\t\\n/************************************************************************/\\nvar __webpack_exports__ = {};\\n\\n// NAMESPACE OBJECT: ./src/marginal1d/callbacks.ts\\nvar callbacks_namespaceObject = {};\\n__webpack_require__.r(callbacks_namespaceObject);\\n__webpack_require__.d(callbacks_namespaceObject, {\\n  \\\"computeData\\\": () =&gt; (computeData),\\n  \\\"computeStats\\\": () =&gt; (computeStats),\\n  \\\"update\\\": () =&gt; (update),\\n  \\\"updateAxisLabel\\\": () =&gt; (updateAxisLabel)\\n});\\n\\n;// CONCATENATED MODULE: ./src/stats/array.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n/**\\n * Syntactic sugar for summing an array of numbers.\\n *\\n * @param {number[]} data - The array of data.\\n * @returns {number} The sum of the array of data.\\n */\\nconst arraySum = (data) =&gt; {\\n    return data.reduce((previousValue, currentValue) =&gt; {\\n        return previousValue + currentValue;\\n    });\\n};\\n/**\\n * Calculate the mean of the given array of data.\\n *\\n * @param {number[]} data - The array of data.\\n * @returns {number} The mean of the given data.\\n */\\nconst arrayMean = (data) =&gt; {\\n    const dataSum = arraySum(data);\\n    return dataSum / data.length;\\n};\\n/**\\n * Cumulative sum of the given data.\\n *\\n * @param {number[]} data - Any array of data.\\n * @returns {number[]} The cumulative sum of the given data.\\n */\\nconst cumulativeSum = (data) =&gt; {\\n    // eslint-disable-next-line arrow-body-style\\n    const cumulativeSumMap = ((sum) =&gt; (value) =&gt; {\\n        // eslint-disable-next-line no-return-assign\\n        return (sum += value);\\n    })(0);\\n    const cSum = data.map(cumulativeSumMap);\\n    const normalizationFactor = Math.max(...cSum);\\n    const cumSum = [];\\n    for (let i = 0; i &lt; cSum.length; i += 1) {\\n        cumSum.push(cSum[i] / normalizationFactor);\\n    }\\n    return cumSum;\\n};\\n/**\\n * Numerically sort the given array of numbers.\\n *\\n * @param {number[]} data - The array of numbers to sort.\\n * @returns {number[]} The sorted array of numbers.\\n */\\nconst numericalSort = (data) =&gt; {\\n    const dataCopy = data.slice(0);\\n    return dataCopy.sort((a, b) =&gt; {\\n        return a &lt; b ? -1 : a &gt; b ? 1 : 0;\\n    });\\n};\\n/**\\n * Determine the shape of the given array.\\n *\\n * @param {any[]} data - Any array of data.\\n * @returns {number[]} The shape of the data as an array.\\n */\\nconst shape = (data) =&gt; {\\n    // From https://stackoverflow.com/questions/10237615/get-size-of-dimensions-in-array\\n    const computeShape = (array) =&gt; {\\n        return array.length ? [...[array.length], ...computeShape(array[0])] : [];\\n    };\\n    const arrayShape = computeShape(data);\\n    // Remove the empty array that will exist at the end of the shape array, since it is\\n    // the returned \\\"else\\\" value from above.\\n    const dataShape = [];\\n    for (let i = 0; i &lt; arrayShape.length; i += 1) {\\n        if (!Array.isArray(arrayShape[i])) {\\n            dataShape.push(arrayShape[i]);\\n        }\\n    }\\n    return dataShape;\\n};\\n/**\\n * Create an array that starts and stops with the given number of steps.\\n *\\n * @param {number} start - Where to start the array from.\\n * @param {number} stop - Where to stop the array.\\n * @param {number} [step] - The step size to take.\\n * @param {boolean} [closed] - Flag used to return a closed array or not.\\n * @param {null | number} [size] - If not null, then will return an array with the given\\n *     size.\\n * @returns {number[]} An array that is linearly spaced between the start and stop\\n *     values.\\n */\\nconst linearRange = (start, stop, step = 1, closed = true, size = null) =&gt; {\\n    if (size !== null) {\\n        step = (stop - start) / size;\\n    }\\n    let len = (stop - start) / step + 1;\\n    if (!closed) {\\n        len = (stop - start - step) / step + 1;\\n    }\\n    return Array.from({ length: len }, (_, i) =&gt; {\\n        return start + i * step;\\n    });\\n};\\n/**\\n * Return the indices that would sort the array. Follows NumPy's implementation.\\n *\\n * @param {number[]} data - The data to sort.\\n * @returns {number[]} An array of indices that would sort the original array.\\n */\\nconst array_argSort = (data) =&gt; {\\n    const dataCopy = data.slice(0);\\n    return dataCopy\\n        .map((value, index) =&gt; {\\n        return [value, index];\\n    })\\n        .sort((a, b) =&gt; {\\n        return a[0] - b[0];\\n    })\\n        .map((value) =&gt; {\\n        return value[1];\\n    });\\n};\\n/**\\n * Count the number of time a value appears in an array.\\n *\\n * @param {number[]} data - The numeric array to count objects for.\\n * @returns {{[key: string]: number}} An object that contains the keys as the items in\\n *     the original array, and values that are counts of the key.\\n */\\nconst array_valueCounts = (data) =&gt; {\\n    const counts = {};\\n    for (let i = 0; i &lt; data.length; i += 1) {\\n        counts[data[i]] = (counts[data[i]] || 0) + 1;\\n    }\\n    return counts;\\n};\\n/**\\n * Calculate the median value for the given array.\\n *\\n * @param {number[]} data - Numerical array of data.\\n * @returns {number} The median value of the given data.\\n */\\nconst arrayMedian = (data) =&gt; {\\n    const sortedArray = numericalSort(data);\\n    const arrayLength = sortedArray.length;\\n    const isEven = sortedArray.length % 2 === 0;\\n    let median;\\n    if (isEven) {\\n        const index = arrayLength / 2;\\n        median = (sortedArray[index - 1] + sortedArray[index]) / 2;\\n    }\\n    else {\\n        median = sortedArray[Math.floor(arrayLength / 2)];\\n    }\\n    return median;\\n};\\n\\n;// CONCATENATED MODULE: ./src/stats/dataTransformation.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n/**\\n * Scale the given array of numbers by the given scaleFactor. Note that this method\\n * divides values in the given array by the scaleFactor.\\n *\\n * @param {number[]} data - An array of numbers that needs to be scaled.\\n * @param {number} scaleFactor - The value to divide all array values by.\\n * @returns {number[]} The scaled array.\\n */\\nconst scaleBy = (data, scaleFactor) =&gt; {\\n    const scaledData = data.map((datum) =&gt; {\\n        return datum / scaleFactor;\\n    });\\n    return scaledData;\\n};\\n/**\\n * Scale the given array to a maximum value of 1. This method uses the scaleBy method,\\n * and finds the maximum value of the given array to use as the scaleFactor of the\\n * scaleBy method.\\n *\\n * @param {number[]} data - Array of data to scale to one.\\n * @returns {number[]} The data scaled to one.\\n */\\nconst scaleToOne = (data) =&gt; {\\n    const scaleFactor = Math.max(...data);\\n    return scaleBy(data, scaleFactor);\\n};\\n/**\\n * Assign ranks to the given data. Follows SciPy's and ArviZ's implementations.\\n *\\n * @param {number[]} data - The numeric data to rank.\\n * @returns {number[]} An array of rankings.\\n */\\nconst rankData = (data) =&gt; {\\n    const n = data.length;\\n    const rank = Array(n);\\n    const sortedIndex = argSort(data);\\n    for (let i = 0; i &lt; rank.length; i += 1) {\\n        rank[sortedIndex[i]] = i + 1;\\n    }\\n    const counts = valueCounts(data);\\n    const countsArray = Object.entries(counts);\\n    const keys = [];\\n    const keyCounts = [];\\n    for (let i = 0; i &lt; countsArray.length; i += 1) {\\n        const [key, count] = countsArray[i];\\n        if (count &gt; 1) {\\n            keys.push(parseFloat(key));\\n            keyCounts.push(count);\\n        }\\n    }\\n    for (let i = 0; i &lt; keys.length; i += 1) {\\n        const repeatIndices = [];\\n        for (let j = 0; j &lt; data.length; j += 1) {\\n            if (data[j] === keys[i]) {\\n                repeatIndices.push(j);\\n            }\\n        }\\n        const rankValues = [];\\n        for (let k = 0; k &lt; repeatIndices.length; k += 1) {\\n            rankValues.push(rank[repeatIndices[k]]);\\n        }\\n        const sum = rankValues.reduce((previousValue, currentValue) =&gt; {\\n            return previousValue + currentValue;\\n        }, 0.0);\\n        const rankMean = sum / rankValues.length;\\n        for (let k = 0; k &lt; repeatIndices.length; k += 1) {\\n            rank[repeatIndices[k]] = rankMean;\\n        }\\n    }\\n    return rank;\\n};\\n\\n;// CONCATENATED MODULE: ./src/stats/highestDensityInterval.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n/**\\n * Find the lower and upper bound of the Highest Density Interval (HDI) for the given\\n * data.\\n *\\n * @param {number[]} data - Raw random variable data from the model.\\n * @param {number} hdiProbability - The highest density interval probability to use when\\n *     calculating the HDI.\\n * @returns {HdiInterval} An object defining the lower and upper bound for the HDI of\\n *     the given data, as well as the indices of the lower and upper bound for the\\n *     sorted raw data.\\n */\\nconst interval = (data, hdiProbability) =&gt; {\\n    const N = data.length;\\n    const sortedData = numericalSort(data);\\n    const stopIndex = Math.floor(hdiProbability * N);\\n    const startIndex = N - stopIndex;\\n    const leftData = sortedData.slice(stopIndex);\\n    const rightData = sortedData.slice(0, startIndex);\\n    const hdi = [];\\n    for (let i = 0; i &lt; leftData.length; i += 1) {\\n        hdi.push(leftData[i] - rightData[i]);\\n    }\\n    const lowerIndex = hdi.indexOf(Math.min(...hdi));\\n    const upperIndex = lowerIndex + stopIndex;\\n    return {\\n        lowerBound: sortedData[lowerIndex],\\n        upperBound: sortedData[upperIndex],\\n        lowerBoundIndex: lowerIndex,\\n        upperBoundIndex: upperIndex,\\n    };\\n};\\n/**\\n * Constructs x and y arrays from the HDI bounds using the raw random variable data. It\\n * also returns the actual HDI bounds of the random variable data.\\n *\\n * @param {number[]} rvData - Raw random variable data from the model.\\n * @param {number[]} marginalX - The support of the Kernel Density Estimate of the\\n *     random variable.\\n * @param {number[]} marginalY - The Kernel Density Estimate of the random variable.\\n * @param {number} hdiProbability - The highest density interval probability to use when\\n *     calculating the HDI.\\n * @returns {HdiData} The x array for the HDI data is the \\\"lower\\\" key, while the y array\\n *     for the HDI data is the \\\"upper\\\" key and the \\\"base\\\" key is the support along the\\n *     axis the HDI is calculated. This nomenclature mirrors the Bokeh Band object,\\n *     which is the annotation type we will use when rendering the HDI intervals.\\n */\\nconst highestDensityInterval_data = (rvData, marginalX, marginalY, hdiProbability) =&gt; {\\n    const hdi = interval(rvData, hdiProbability);\\n    const base = [];\\n    const upper = [];\\n    for (let i = 0; i &lt; marginalX.length; i += 1) {\\n        if (marginalX[i] &lt;= hdi.upperBound &amp;&amp; marginalX[i] &gt;= hdi.lowerBound) {\\n            base.push(marginalX[i]);\\n            upper.push(marginalY[i]);\\n        }\\n    }\\n    // NOTE: We mirror the Bokeh Band annotation object key names below. The lower key is\\n    //       an array of zeros since we will fill the Band all the way to the lower axis.\\n    return {\\n        base: base,\\n        lower: new Array(base.length).fill(0.0),\\n        upper: upper,\\n        lowerBound: hdi.lowerBound,\\n        upperBound: hdi.upperBound,\\n    };\\n};\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/accessor.js\\nfunction accessor(x, fallback) {\\n  return x == null ? fallback\\n    : typeof x === 'function' ? x\\n    : d =&gt; d[x];\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/bin1d.js\\nfunction bin1d(data, x, weight, lo, hi, n) {\\n  const grid = new Float64Array(n);\\n  const delta = (n - 1) / (hi - lo);\\n\\n  for (let i = 0; i &lt; data.length; ++i) {\\n    const d = data[i];\\n    const xi = x(d, i, data);\\n    const wi = weight(d, i, data);\\n\\n    // skip NaN and Infinite values\\n    if (!(Number.isFinite(xi) &amp;&amp; Number.isFinite(wi))) {\\n      continue;\\n    }\\n\\n    const p = (xi - lo) * delta;\\n    const u = Math.floor(p);\\n    const v = u + 1;\\n\\n    if (0 &lt;= u &amp;&amp; v &lt; n) {\\n      grid[u] += (v - p) * wi;\\n      grid[v] += (p - u) * wi;\\n    } else if (u === -1) {\\n      grid[v] += (p - u) * wi;\\n    } else if (v === n) {\\n      grid[u] += (v - p) * wi;\\n    }\\n  }\\n\\n  return grid;\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/deriche.js\\n// Deriche's approximation of Gaussian smoothing\\n// Adapted from Getreuer's C implementation (BSD license)\\n// https://www.ipol.im/pub/art/2013/87/gaussian_20131215.tgz\\n// http://dev.ipol.im/~getreuer/code/doc/gaussian_20131215_doc/gaussian__conv__deriche_8c.html\\n\\nfunction dericheConfig(sigma, negative = false) {\\n  // compute causal filter coefficients\\n  const a = new Float64Array(5);\\n  const bc = new Float64Array(4);\\n  dericheCausalCoeff(a, bc, sigma);\\n\\n  // numerator coefficients of the anticausal filter\\n  const ba = Float64Array.of(\\n    0,\\n    bc[1] - a[1] * bc[0],\\n    bc[2] - a[2] * bc[0],\\n    bc[3] - a[3] * bc[0],\\n    -a[4] * bc[0]\\n  );\\n\\n  // impulse response sums\\n  const accum_denom = 1.0 + a[1] + a[2] + a[3] + a[4];\\n  const sum_causal = (bc[0] + bc[1] + bc[2] + bc[3]) / accum_denom;\\n  const sum_anticausal = (ba[1] + ba[2] + ba[3] + ba[4]) / accum_denom;\\n\\n  // coefficients object\\n  return {\\n    sigma,\\n    negative,\\n    a,\\n    b_causal: bc,\\n    b_anticausal: ba,\\n    sum_causal,\\n    sum_anticausal\\n  };\\n}\\n\\nfunction dericheCausalCoeff(a_out, b_out, sigma) {\\n  const K = 4;\\n\\n  const alpha = Float64Array.of(\\n    0.84, 1.8675,\\n    0.84, -1.8675,\\n    -0.34015, -0.1299,\\n    -0.34015, 0.1299\\n  );\\n\\n  const x1 = Math.exp(-1.783 / sigma);\\n  const x2 = Math.exp(-1.723 / sigma);\\n  const y1 = 0.6318 / sigma;\\n  const y2 = 1.997 / sigma;\\n  const beta = Float64Array.of(\\n    -x1 * Math.cos( y1), x1 * Math.sin( y1),\\n    -x1 * Math.cos(-y1), x1 * Math.sin(-y1),\\n    -x2 * Math.cos( y2), x2 * Math.sin( y2),\\n    -x2 * Math.cos(-y2), x2 * Math.sin(-y2)\\n  );\\n\\n  const denom = sigma * 2.5066282746310007;\\n\\n  // initialize b/a = alpha[0] / (1 + beta[0] z^-1)\\n  const b = Float64Array.of(alpha[0], alpha[1], 0, 0, 0, 0, 0, 0);\\n  const a = Float64Array.of(1, 0, beta[0], beta[1], 0, 0, 0, 0, 0, 0);\\n\\n  let j, k;\\n\\n  for (k = 2; k &lt; 8; k += 2) {\\n    // add kth term, b/a += alpha[k] / (1 + beta[k] z^-1)\\n    b[k]     = beta[k] * b[k - 2] - beta[k + 1] * b[k - 1];\\n    b[k + 1] = beta[k] * b[k - 1] + beta[k + 1] * b[k - 2];\\n    for (j = k - 2; j &gt; 0; j -= 2) {\\n      b[j]     += beta[k] * b[j - 2] - beta[k + 1] * b[j - 1];\\n      b[j + 1] += beta[k] * b[j - 1] + beta[k + 1] * b[j - 2];\\n    }\\n    for (j = 0; j &lt;= k; j += 2) {\\n      b[j]     += alpha[k] * a[j]     - alpha[k + 1] * a[j + 1];\\n      b[j + 1] += alpha[k] * a[j + 1] + alpha[k + 1] * a[j];\\n    }\\n\\n    a[k + 2] = beta[k] * a[k]     - beta[k + 1] * a[k + 1];\\n    a[k + 3] = beta[k] * a[k + 1] + beta[k + 1] * a[k];\\n    for (j = k; j &gt; 0; j -= 2) {\\n      a[j]     += beta[k] * a[j - 2] - beta[k + 1] * a[j - 1];\\n      a[j + 1] += beta[k] * a[j - 1] + beta[k + 1] * a[j - 2];\\n    }\\n  }\\n\\n  for (k = 0; k &lt; K; ++k) {\\n    j = k &lt;&lt; 1;\\n    b_out[k] = b[j] / denom;\\n    a_out[k + 1] = a[j + 2];\\n  }\\n}\\n\\nfunction dericheConv2d(cx, cy, grid, [nx, ny]) {\\n  // allocate buffers\\n  const yc = new Float64Array(Math.max(nx, ny)); // causal\\n  const ya = new Float64Array(Math.max(nx, ny)); // anticausal\\n  const h = new Float64Array(5);\\n  const d = new Float64Array(grid.length);\\n\\n  // convolve rows\\n  for (let row = 0, r0 = 0; row &lt; ny; ++row, r0 += nx) {\\n    const dx = d.subarray(r0);\\n    dericheConv1d(cx, grid.subarray(r0), nx, 1, yc, ya, h, dx);\\n  }\\n\\n  // convolve columns\\n  for (let c0 = 0; c0 &lt; nx; ++c0) {\\n    const dy = d.subarray(c0);\\n    dericheConv1d(cy, dy, ny, nx, yc, ya, h, dy);\\n  }\\n\\n  return d;\\n}\\n\\nfunction dericheConv1d(\\n  c, src, N,\\n  stride = 1,\\n  y_causal = new Float64Array(N),\\n  y_anticausal = new Float64Array(N),\\n  h = new Float64Array(5),\\n  d = y_causal,\\n  init = dericheInitZeroPad\\n) {\\n  const stride_2 = stride * 2;\\n  const stride_3 = stride * 3;\\n  const stride_4 = stride * 4;\\n  const stride_N = stride * N;\\n  let i, n;\\n\\n  // initialize causal filter on the left boundary\\n  init(\\n    y_causal, src, N, stride,\\n    c.b_causal, 3, c.a, 4, c.sum_causal, h, c.sigma\\n  );\\n\\n  // filter the interior samples using a 4th order filter. Implements:\\n  // for n = K, ..., N - 1,\\n  //   y^+(n) = \\\\sum_{k=0}^{K-1} b^+_k src(n - k)\\n  //          - \\\\sum_{k=1}^K a_k y^+(n - k)\\n  // variable i tracks the offset to the nth sample of src, it is\\n  // updated together with n such that i = stride * n.\\n  for (n = 4, i = stride_4; n &lt; N; ++n, i += stride) {\\n    y_causal[n] = c.b_causal[0] * src[i]\\n      + c.b_causal[1] * src[i - stride]\\n      + c.b_causal[2] * src[i - stride_2]\\n      + c.b_causal[3] * src[i - stride_3]\\n      - c.a[1] * y_causal[n - 1]\\n      - c.a[2] * y_causal[n - 2]\\n      - c.a[3] * y_causal[n - 3]\\n      - c.a[4] * y_causal[n - 4];\\n  }\\n\\n  // initialize the anticausal filter on the right boundary\\n  init(\\n    y_anticausal, src, N, -stride,\\n    c.b_anticausal, 4, c.a, 4, c.sum_anticausal, h, c.sigma\\n  );\\n\\n  // similar to the causal filter above, the following implements:\\n  // for n = K, ..., N - 1,\\n  //   y^-(n) = \\\\sum_{k=1}^K b^-_k src(N - n - 1 - k)\\n  //          - \\\\sum_{k=1}^K a_k y^-(n - k)\\n  // variable i is updated such that i = stride * (N - n - 1).\\n  for (n = 4, i = stride_N - stride * 5; n &lt; N; ++n, i -= stride) {\\n    y_anticausal[n] = c.b_anticausal[1] * src[i + stride]\\n      + c.b_anticausal[2] * src[i + stride_2]\\n      + c.b_anticausal[3] * src[i + stride_3]\\n      + c.b_anticausal[4] * src[i + stride_4]\\n      - c.a[1] * y_anticausal[n - 1]\\n      - c.a[2] * y_anticausal[n - 2]\\n      - c.a[3] * y_anticausal[n - 3]\\n      - c.a[4] * y_anticausal[n - 4];\\n  }\\n\\n  // sum the causal and anticausal responses to obtain the final result\\n  if (c.negative) {\\n    // do not threshold if the input grid includes negatively weighted values\\n    for (n = 0, i = 0; n &lt; N; ++n, i += stride) {\\n      d[i] = y_causal[n] + y_anticausal[N - n - 1];\\n    }\\n  } else {\\n    // threshold to prevent small negative values due to floating point error\\n    for (n = 0, i = 0; n &lt; N; ++n, i += stride) {\\n      d[i] = Math.max(0, y_causal[n] + y_anticausal[N - n - 1]);\\n    }\\n  }\\n\\n  return d;\\n}\\n\\nfunction dericheInitZeroPad(dest, src, N, stride, b, p, a, q, sum, h) {\\n  const stride_N = Math.abs(stride) * N;\\n  const off = stride &lt; 0 ? stride_N + stride : 0;\\n  let i, n, m;\\n\\n  // compute the first q taps of the impulse response, h_0, ..., h_{q-1}\\n  for (n = 0; n &lt; q; ++n) {\\n    h[n] = (n &lt;= p) ? b[n] : 0;\\n    for (m = 1; m &lt;= q &amp;&amp; m &lt;= n; ++m) {\\n      h[n] -= a[m] * h[n - m];\\n    }\\n  }\\n\\n  // compute dest_m = sum_{n=1}^m h_{m-n} src_n, m = 0, ..., q-1\\n  // note: q == 4\\n  for (m = 0; m &lt; q; ++m) {\\n    for (dest[m] = 0, n = 1; n &lt;= m; ++n) {\\n      i = off + stride * n;\\n      if (i &gt;= 0 &amp;&amp; i &lt; stride_N) {\\n        dest[m] += h[m - n] * src[i];\\n      }\\n    }\\n  }\\n\\n  // dest_m = dest_m + h_{n+m} src_{-n}\\n  const cur = src[off];\\n  if (cur &gt; 0) {\\n    for (m = 0; m &lt; q; ++m) {\\n      dest[m] += h[m] * cur;\\n    }\\n  }\\n\\n  return;\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/extent.js\\nfunction extent(data, x, pad = 0) {\\n  const n = data.length;\\n  let lo;\\n  let hi;\\n  for (let i = 0; i &lt; n; ++i) {\\n    const v = x(data[i], i, data);\\n    if (v != null) {\\n      if (lo === undefined) {\\n        if (v &gt;= v) lo = hi = v;\\n      } else {\\n        if (v &lt; lo) lo = v;\\n        if (v &gt; hi) hi = v;\\n      }\\n    }\\n  }\\n  return [lo - pad, hi + pad];\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/nrd.js\\n// Scott, D. W. (1992) Multivariate Density Estimation:\\n// Theory, Practice, and Visualization. Wiley.\\nfunction nrd(data, x) {\\n  const values = data.map(x).filter(v =&gt; v != null &amp;&amp; v &gt;= v);\\n  values.sort((a, b) =&gt; a - b);\\n  const sd = stdev(values);\\n  const q1 = quantile(values, 0.25);\\n  const q3 = quantile(values, 0.75);\\n\\n  const n = values.length,\\n        h = (q3 - q1) / 1.34,\\n        v = Math.min(sd, h) || sd || Math.abs(q1) || 1;\\n\\n  return 1.06 * v * Math.pow(n, -0.2);\\n}\\n\\nfunction stdev(values) {\\n  const n = values.length;\\n  let count = 0;\\n  let delta;\\n  let mean = 0;\\n  let sum = 0;\\n  for (let i = 0; i &lt; n; ++i) {\\n    const value = values[i];\\n    delta = value - mean;\\n    mean += delta / ++count;\\n    sum += delta * (value - mean);\\n  }\\n  return count &gt; 1 ? Math.sqrt(sum / (count - 1)) : NaN;\\n}\\n\\nfunction quantile(values, p) {\\n  const n = values.length;\\n\\n  if (!n) return NaN;\\n  if ((p = +p) &lt;= 0 || n &lt; 2) return values[0];\\n  if (p &gt;= 1) return values[n - 1];\\n\\n  const i = (n - 1) * p;\\n  const i0 = Math.floor(i);\\n  const v0 = values[i0];\\n  return v0 + (values[i0 + 1] - v0) * (i - i0);\\n}\\n\\n;// CONCATENATED MODULE: ./node_modules/fast-kde/src/density1d.js\\n\\n\\n\\n\\n\\n\\nfunction density1d(data, options = {}) {\\n  const { adjust = 1, pad = 3, bins = 512 } = options;\\n  const x = accessor(options.x, x =&gt; x);\\n  const w = accessor(options.weight, () =&gt; 1 / data.length);\\n\\n  let bandwidth = options.bandwidth ?? adjust * nrd(data, x);\\n\\n  const [lo, hi] = options.extent ?? extent(data, x, pad * bandwidth);\\n  const grid = bin1d(data, x, w, lo, hi, bins);\\n  const delta = (hi - lo) / (bins - 1);\\n  const neg = grid.some(v =&gt; v &lt; 0);\\n\\n  let config = dericheConfig(bandwidth / delta, neg);\\n  let result;\\n\\n  function* points(x = 'x', y = 'y') {\\n    const result = estimator.grid();\\n    const scale = 1 / delta;\\n    for (let i = 0; i &lt; bins; ++i) {\\n      yield {\\n        [x]: lo + i * delta,\\n        [y]: result[i] * scale\\n      };\\n    }\\n  }\\n\\n  const estimator = {\\n    [Symbol.iterator]: points,\\n    points,\\n    grid: () =&gt; result || (result = dericheConv1d(config, grid, bins)),\\n    extent: () =&gt; [lo, hi],\\n    bandwidth(_) {\\n      if (arguments.length) {\\n        if (_ !== bandwidth) {\\n          bandwidth = _;\\n          result = null;\\n          config = dericheConfig(bandwidth / delta, neg);\\n        }\\n        return estimator;\\n      } else {\\n        return bandwidth;\\n      }\\n    }\\n  };\\n\\n  return estimator;\\n}\\n\\n;// CONCATENATED MODULE: ./src/stats/marginal.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n\\n/**\\n * Calculate the one-dimensional Kernel Density Estimate. This method uses the \\\"scott\\\"\\n * estimate for the bandwidth used when calculating the marginal. This bandwidth\\n * estimate is the same as is used in the corresponding Python KDE estimate from ArviZ.\\n *\\n * Scott, D (1979). On optimal and data-based histograms. Biometrika. 66 (3): 605\\u2013610.\\n * doi:10.1093/biomet/66.3.605.\\n *\\n * @param {number[]} data - The raw random variable data of the model.\\n * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when\\n *     calculating the Kernel Density Estimate (KDE).\\n * @returns {{x: number[]; y: number[]; bandwidth: number}} The KDE of the given data,\\n *     along with the numerical value of the bandwidth used to calculate the KDE.\\n */\\n// eslint-disable-next-line import/prefer-default-export\\nconst oneD = (data, bwFactor) =&gt; {\\n    // Calculate the 1D KDE.\\n    const kde1d = density1d(data, {\\n        adjust: bwFactor,\\n        pad: 3,\\n        bins: 512,\\n    });\\n    // Extract the data points from the 1D KDE calculation.\\n    const points = Array.from(kde1d);\\n    const x = [];\\n    const y = [];\\n    for (let i = 0; i &lt; points.length; i += 1) {\\n        x.push(points[i].x);\\n        y.push(points[i].y);\\n    }\\n    return {\\n        x: x,\\n        y: scaleToOne(y),\\n        bandwidth: kde1d.bandwidth(),\\n    };\\n};\\n\\n;// CONCATENATED MODULE: ./src/stats/utils.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\nconst left_edge_index = (point, intervals) =&gt; {\\n    const len = intervals.length;\\n    if (point &lt; intervals[0]) {\\n        return -1;\\n    }\\n    if (point &gt; intervals[len - 1]) {\\n        return intervals.length;\\n    }\\n    let leftEdgeIndex = 0;\\n    let rightEdgeIndex = len - 1;\\n    while (rightEdgeIndex - leftEdgeIndex !== 1) {\\n        const indexOfNumberToCompare = leftEdgeIndex + Math.floor((rightEdgeIndex - leftEdgeIndex) / 2);\\n        if (point &gt;= intervals[indexOfNumberToCompare])\\n            leftEdgeIndex = indexOfNumberToCompare;\\n        else\\n            rightEdgeIndex = indexOfNumberToCompare;\\n    }\\n    return leftEdgeIndex;\\n};\\nconst lerp = (x, x0, y0, x1, y1) =&gt; {\\n    const slope = (y1 - y0) / (x1 - x0);\\n    let res = slope * (x - x0) + y0;\\n    if (!Number.isFinite(res)) {\\n        res = slope * (x - x1) + y1;\\n        if (!Number.isFinite(res) &amp;&amp; y0 === y1)\\n            res = y0;\\n    }\\n    return res;\\n};\\nconst interpolate = (points, x, y) =&gt; {\\n    // Implementation ported from np.interp\\n    const n = points.length;\\n    const results = new Array(n);\\n    for (let i = 0; i &lt; n; i += 1) {\\n        const point = points[i];\\n        if (Number.isNaN(point)) {\\n            results[i] = point;\\n            // eslint-disable-next-line no-continue\\n            continue;\\n        }\\n        const index = left_edge_index(point, x);\\n        if (index === -1) {\\n            // eslint-disable-next-line prefer-destructuring\\n            results[i] = y[0];\\n        }\\n        else if (index === x.length) {\\n            results[i] = y[y.length - 1];\\n        }\\n        else if (index === x.length - 1 || x[index] === point) {\\n            results[i] = y[index];\\n        }\\n        else {\\n            const x0 = x[index];\\n            const y0 = y[index];\\n            const x1 = x[index + 1];\\n            const y1 = y[index + 1];\\n            results[i] = lerp(point, x0, y0, x1, y1);\\n        }\\n    }\\n    return results;\\n};\\n// eslint-disable-next-line import/prefer-default-export\\nconst interpolatePoints = ({ x, y, points, }) =&gt; {\\n    const output = [];\\n    for (let i = 0; i &lt; points.length; i += 1) {\\n        const interpolatedPoint = Array.from(interpolate([points[i]], x, y));\\n        output.push(interpolatedPoint[0]);\\n    }\\n    return output;\\n};\\n\\n;// CONCATENATED MODULE: ./src/marginal1d/callbacks.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n\\n\\n\\n\\n// Define the names of the figures used for this Bokeh application.\\nconst figureNames = ['marginal', 'cumulative'];\\n/**\\n * Update the given Bokeh Axis object with the new label string. You must use this\\n * method to update axis strings using TypeScript, otherwise the ts compiler will throw\\n * a type check error.\\n *\\n * @param {Axis} axis - The Bokeh Axis object needing a new label.\\n * @param {string | null} label - The new label for the Bokeh Axis object.\\n */\\nconst updateAxisLabel = (axis, label) =&gt; {\\n    // Type check requirement.\\n    if ('axis_label' in axis) {\\n        axis.axis_label = label;\\n    }\\n};\\n/**\\n * Compute the following statistics for the given random variable data\\n *\\n * - lower bound for the highest density interval calculated from the marginalX;\\n * - mean of the rawData;\\n * - upper bound for the highest density interval calculated from the marginalY.\\n *\\n * @param {number[]} rawData - Raw random variable data from the model.\\n * @param {number[]} marginalX - The support of the Kernel Density Estimate of the\\n *     random variable.\\n * @param {number[]} marginalY - The Kernel Density Estimate of the random variable.\\n * @param {number} activeStatistic - The statistic to show in the tool. 0 is the mean\\n *     and 1 is the median.\\n * @param {number | null} [hdiProb=null] - The highest density interval probability\\n *     value. If the default value is not overwritten, then the default HDI probability\\n *     is 0.89. See Statistical Rethinking by McElreath for a description as to why this\\n *     value is the default.\\n * @param {string[]} [text_align=['right', 'center', 'left']] - How to align the text\\n *     shown in the figure for the point statistics.\\n * @param {number[]} [x_offset=[-5, 0, 5]] - Offset values for the text along the\\n *     x-axis.\\n * @param {number[]} [y_offset=[0, 10, 0]] - Offset values for the text along the\\n *     y-axis\\n * @returns {interfaces.LabelsData} Object containing the computed stats.\\n */\\nconst computeStats = (rawData, marginalX, marginalY, activeStatistic, hdiProb = null, text_align = ['right', 'center', 'left'], x_offset = [-5, 0, 5], y_offset = [0, 10, 0]) =&gt; {\\n    // Set the default value to 0.89 if no default value has been given.\\n    const hdiProbability = hdiProb ?? 0.89;\\n    // Compute the point statistics for the KDE, and create labels to display them in the\\n    // figures.\\n    const mean = arrayMean(rawData);\\n    const median = arrayMedian(rawData);\\n    const hdiBounds = interval(rawData, hdiProbability);\\n    let x = [hdiBounds.lowerBound, mean, median, hdiBounds.upperBound];\\n    let y = interpolatePoints({ x: marginalX, y: marginalY, points: x });\\n    let text = [\\n        `Lower HDI: ${hdiBounds.lowerBound.toFixed(3)}`,\\n        `Mean: ${mean.toFixed(3)}`,\\n        `Median: ${median.toFixed(3)}`,\\n        `Upper HDI: ${hdiBounds.upperBound.toFixed(3)}`,\\n    ];\\n    // We will filter the output based on the active statistic from the tool.\\n    let mask = [];\\n    if (activeStatistic === 0) {\\n        mask = [0, 1, 3];\\n    }\\n    else if (activeStatistic === 1) {\\n        mask = [0, 2, 3];\\n    }\\n    x = mask.map((i) =&gt; {\\n        return x[i];\\n    });\\n    y = mask.map((i) =&gt; {\\n        return y[i];\\n    });\\n    text = mask.map((i) =&gt; {\\n        return text[i];\\n    });\\n    const output = {\\n        x: x,\\n        y: y,\\n        text: text,\\n        text_align: text_align,\\n        x_offset: x_offset,\\n        y_offset: y_offset,\\n    };\\n    return output;\\n};\\n/**\\n * Compute data for the one-dimensional marginal diagnostic tool.\\n *\\n * @param {number[]} data - Raw random variable data from the model.\\n * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when\\n *     calculating the Kernel Density Estimate (KDE).\\n * @param {number} hdiProbability - The highest density interval probability to use when\\n *     calculating the HDI.\\n * @param {number} activeStatistic - The statistic to show in the tool. 0 is the mean\\n *     and 1 is the median.\\n * @returns {interfaces.Data} The marginal distribution and cumulative\\n *     distribution calculated from the given random variable data. Point statistics are\\n *     also calculated.\\n */\\nconst computeData = (data, bwFactor, hdiProbability, activeStatistic) =&gt; {\\n    const output = {};\\n    for (let i = 0; i &lt; figureNames.length; i += 1) {\\n        const figureName = figureNames[i];\\n        output[figureName] = {};\\n        // Compute the one-dimensional KDE and its cumulative distribution.\\n        const distribution = oneD(data, bwFactor);\\n        switch (figureName) {\\n            case 'cumulative':\\n                distribution.y = scaleToOne(cumulativeSum(distribution.y));\\n                break;\\n            default:\\n                break;\\n        }\\n        // Compute the point statistics for the given data.\\n        const stats = computeStats(data, distribution.x, distribution.y, activeStatistic, hdiProbability);\\n        output[figureName] = {\\n            distribution: distribution,\\n            hdi: highestDensityInterval_data(data, distribution.x, distribution.y, hdiProbability),\\n            stats: { x: stats.x, y: stats.y, text: stats.text },\\n            labels: stats,\\n        };\\n    }\\n    return output;\\n};\\n/**\\n * Callback used to update the Bokeh application in the notebook.\\n *\\n * @param {number[]} data - Raw random variable data from the model.\\n * @param {string} rvName - The name of the random variable from the model.\\n * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when\\n *     calculating the kernel density estimate.\\n * @param {number} hdiProbability - The highest density interval probability to use when\\n *     calculating the HDI.\\n * @param {interfaces.Sources} sources - Bokeh sources used to render glyphs in the\\n *     application.\\n * @param {interfaces.Figures} figures - Bokeh figures shown in the application.\\n * @param {interfaces.Tooltips} tooltips - Bokeh tooltips shown on the glyphs.\\n * @param {interfaces.Widgets} widgets - Bokeh widget object for the tool.\\n * @returns {number} We display the value of the bandwidth used for computing the Kernel\\n *     Density Estimate in a div, and must return that value here in order to update the\\n *     value displayed to the user.\\n */\\nconst update = (data, rvName, bwFactor, hdiProbability, sources, figures, tooltips, widgets) =&gt; {\\n    const activeStatistic = widgets.stats_button.active;\\n    const computedData = computeData(data, bwFactor, hdiProbability, activeStatistic);\\n    // Marginal figure.\\n    // eslint-disable-next-line prefer-destructuring\\n    const bandwidth = computedData.marginal.distribution.bandwidth;\\n    sources.marginal.distribution.data = {\\n        x: computedData.marginal.distribution.x,\\n        y: computedData.marginal.distribution.y,\\n    };\\n    sources.marginal.hdi.data = {\\n        base: computedData.marginal.hdi.base,\\n        lower: computedData.marginal.hdi.lower,\\n        upper: computedData.marginal.hdi.upper,\\n    };\\n    sources.marginal.stats.data = computedData.marginal.stats;\\n    sources.marginal.labels.data = computedData.marginal.labels;\\n    tooltips.marginal.distribution.tooltips = [[rvName, '@x']];\\n    tooltips.marginal.stats.tooltips = [['', '@text']];\\n    updateAxisLabel(figures.marginal.below[0], rvName);\\n    // Cumulative figure.\\n    sources.cumulative.distribution.data = {\\n        x: computedData.cumulative.distribution.x,\\n        y: computedData.cumulative.distribution.y,\\n    };\\n    sources.cumulative.hdi.data = {\\n        base: computedData.cumulative.hdi.base,\\n        lower: computedData.cumulative.hdi.lower,\\n        upper: computedData.cumulative.hdi.upper,\\n    };\\n    sources.cumulative.stats.data = computedData.cumulative.stats;\\n    sources.cumulative.labels.data = computedData.cumulative.labels;\\n    tooltips.cumulative.distribution.tooltips = [[rvName, '@x']];\\n    tooltips.cumulative.stats.tooltips = [['', '@text']];\\n    updateAxisLabel(figures.cumulative.below[0], rvName);\\n    return bandwidth;\\n};\\n\\n;// CONCATENATED MODULE: ./src/marginal1d/index.ts\\n/**\\n * Copyright (c) Meta Platforms, Inc. and affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n// The CustomJS methods used by Bokeh require us to make the JavaScript available in the\\n// browser, which is done by defining it below.\\nwindow.marginal1d = callbacks_namespaceObject;\\n\\n/******/ })()\\n;\\n              bw = marginal1d.update(\\n                rvData,\\n                rvName,\\n                bwFactor,\\n                hdiProbability,\\n                sources,\\n                figures,\\n                tooltips,\\n                widgets,\\n              );\\n            }\\n        ;\\n            widgets.bw_div.text = `Bandwidth: ${bwFactor * bw}`;\\n            figures.marginal.reset.emit();\\n        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@@ -2076,8 +2076,8 @@
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-      "CPU times: user 10.5 s, sys: 107 ms, total: 10.6 s\n",
-      "Wall time: 5.06 s\n"
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@@ -2107,12 +2107,12 @@
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+      "CPU times: user 468 ms, sys: 0 ns, total: 468 ms\n",
+      "Wall time: 459 ms\n",
       "\n",
       "0%   10   20   30   40   50   60   70   80   90   100%\n",
       "|----|----|----|----|----|----|----|----|----|----|\n",
-      "*CPU times: user 390 ms, sys: 3.48 ms, total: 393 ms\n",
-      "Wall time: 391 ms\n",
-      "**************************************************\n"
+      "***************************************************\n"
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