add in culture data

DESCRIPTION

@@ -1,13 +1,13 @@
 Package: pald
-Title: Partitioned Local Depths for Community Structure in Data
+Title: Partitioned Local Depth for Community Structure in Data
 Version: 0.0.2
 Authors@R: 
     c(person("Katherine", "Moore", email = "[email protected]", role = c("aut"),
            comment = c(ORCID = "0000-0001-6943-2416")),
     person("Kenneth", "Berenhaut", email = "[email protected]", role = c("aut")),
     person("Lucy", "D'Agostino McGowan", email = "[email protected]",
     role = c("aut", "cre"), comment = c(ORCID = "0000-0001-7297-9359")))
-Description: Implementation of the Partitioned Local Depths (PaLD) 
+Description: Implementation of the Partitioned Local Depth (PaLD) 
     approach which provides a measure of local depth and the cohesion of a point 
     to another which  (together with a universal threshold for distinguishing 
     strong and weak ties) may be used to reveal local and global structure in 

NEWS.md

@@ -1,6 +1,7 @@
-# pald 0.0.1
+# pald 0.0.2
 
-* Allow non-symmetric matrices to be input
+* Allow non-symmetric matrices to be input 
+* Add `cultures` data
 
 # pald 0.0.1
 

R/cultures.R

@@ -0,0 +1,15 @@
+#' Cultures pairwise dissimilarities
+#'
+#' Pairwise dissimilarities are given by the cultural fixation index obtained
+#' from World Values Survey responses.
+#'
+#' @references M. Muthukrishna, et al., Beyond western, educated, industrial,
+#'   rich, and democratic (WEIRD) psychology: measuring and mapping scales of
+#'   cultural and psychological distance. Psychol. Sci. 1, 24 (2020).
+#'
+#'R. Inglehart et al, World Values Survey: All Rounds-Country-Pooled Datafile
+#'  1981-2014, (JD Systems Institute, Madrid 2014).
+#'
+#' @format A `59x59 matrix` of dissimilarities
+#'
+"cultures"

R/pald_functions.R

@@ -478,7 +478,7 @@ community_clusters <- function(c) {
     cluster = cl
   )
 }
-#' Partitioned Local Depths (PaLD)
+#' Partitioned Local Depth (PaLD)
 #'
 #' A wrapper function which computes the cohesion matrix, local depths,
 #' community graphs and provides a plot of the community graphs with connected

README.Rmd

@@ -24,7 +24,7 @@ knitr::opts_chunk$set(
 [![R-CMD-check](https://github.com/LucyMcGowan/pald/actions/workflows/check-standard.yaml/badge.svg)](https://github.com/LucyMcGowan/pald/actions/workflows/check-standard.yaml)
 <!-- badges: end -->
 
-This package provides an implementation of the Partitioned Local Depths (PaLD) approach which consists of a measure of *local depth* and the *cohesion* of a point to another which, together with a universal threshold for distinguishing strong and weak ties, may be used to reveal local and global structure in data.  No extraneous inputs, distributional assumptions, iterative procedures nor optimization criteria are employed. This package includes functions for computing local depths and cohesion matrices as well as flexible functions for plotting community networks and displays of cohesion against distance. 
+This package provides an implementation of the Partitioned Local Depth (PaLD) approach which consists of a measure of *local depth* and the *cohesion* of a point to another which, together with a universal threshold for distinguishing strong and weak ties, may be used to reveal local and global structure in data.  No extraneous inputs, distributional assumptions, iterative procedures nor optimization criteria are employed. This package includes functions for computing local depths and cohesion matrices as well as flexible functions for plotting community networks and displays of cohesion against distance. 
 
 For further discussion of the perspective, including some theoretical results and applications, see: 
 
@@ -52,7 +52,7 @@ library(pald)
 
 ## Input
 
-The input for the Partitioned Local Depths (PaLD) approach is a distance matrix (or `dist` object) associated with a finite collection of data points.  Throughout, no distributional assumptions, iterative procedures nor optimization criteria are employed.  
+The input for the Partitioned Local Depth (PaLD) approach is a distance matrix (or `dist` object) associated with a finite collection of data points.  Throughout, no distributional assumptions, iterative procedures nor optimization criteria are employed.  
 
 The only information extracted from the distance matrix are within-triplet dissimilarity comparisons. As a result, outputs are unaffected by monotone transformations of the collection of distances (e.g., $\log_2$). Further, one may transform any measure of similarity, $s(x, y)$, to a measure of dissimilarity, $d(x,y)$, via any order-reversing monotone transformation, for instance by taking $d(x, y) = 1/(1 + s(x, y))$. This provides the user some flexibility in the choice of dissimilarity (e.g., triangle inequality is not required) and care should be taken at this stage.  
 

README.md

@@ -9,7 +9,7 @@ January 2022
 [![R-CMD-check](https://github.com/LucyMcGowan/pald/actions/workflows/check-standard.yaml/badge.svg)](https://github.com/LucyMcGowan/pald/actions/workflows/check-standard.yaml)
 <!-- badges: end -->
 
-This package provides an implementation of the Partitioned Local Depths
+This package provides an implementation of the Partitioned Local Depth
 (PaLD) approach which consists of a measure of *local depth* and the
 *cohesion* of a point to another which, together with a universal
 threshold for distinguishing strong and weak ties, may be used to reveal
@@ -47,7 +47,7 @@ library(pald)
 
 ## Input
 
-The input for the Partitioned Local Depths (PaLD) approach is a distance
+The input for the Partitioned Local Depth (PaLD) approach is a distance
 matrix (or `dist` object) associated with a finite collection of data
 points. Throughout, no distributional assumptions, iterative procedures
 nor optimization criteria are employed.
@@ -390,7 +390,7 @@ cohesion) and can be found directly from the cohesion matrix.
 library(igraph)
 G_strong_lang <- community_graphs(C_lang)$G_strong
 neighbors(G_strong_lang, "French")
-#> + 8/87 vertices, named, from a44fbe8:
+#> + 8/87 vertices, named, from c8a0516:
 #> [1] Italian         Ladin           Provencal       Walloon        
 #> [5] French_Creole_C French_Creole_D Spanish         Catalan