diff --git a/pelicun/__init__.py b/pelicun/__init__.py
index f78ed32f6..85c77352b 100644
--- a/pelicun/__init__.py
+++ b/pelicun/__init__.py
@@ -41,7 +41,7 @@
 
 name = "pelicun"
 
-__version__ = '3.3.1'
+__version__ = '3.3.2'
 
 __copyright__ = ("Copyright (c) 2018 Leland Stanford "
                  "Junior University and The Regents "
diff --git a/pelicun/resources/auto/Hazus_Earthquake_IM.py b/pelicun/resources/auto/Hazus_Earthquake_IM.py
index 970f51fa3..90f720933 100644
--- a/pelicun/resources/auto/Hazus_Earthquake_IM.py
+++ b/pelicun/resources/auto/Hazus_Earthquake_IM.py
@@ -408,6 +408,11 @@ def auto_populate(AIM):
                     "MapApproach": "Automatic",
                 }
             },
+            "Options": {
+                "NonDirectionalMultipliers": {
+                    "ALL": 1.0
+                },
+            }
         }
 
     elif assetType == "TransportationNetwork":
@@ -441,6 +446,11 @@ def auto_populate(AIM):
                         "MapApproach": "Automatic",
                     }
                 },
+                "Options": {
+                    "NonDirectionalMultipliers": {
+                        "ALL": 1.0
+                    },
+                }
             }
 
         elif inf_type == "HwyTunnel":
@@ -471,6 +481,11 @@ def auto_populate(AIM):
                         "MapApproach": "Automatic",
                     }
                 },
+                "Options": {
+                    "NonDirectionalMultipliers": {
+                        "ALL": 1.0
+                    },
+                }
             }
         elif inf_type == "Roadway":
             # get the road class
@@ -499,6 +514,11 @@ def auto_populate(AIM):
                         "MapApproach": "Automatic",
                     }
                 },
+                "Options": {
+                    "NonDirectionalMultipliers": {
+                        "ALL": 1.0
+                    },
+                }
             }
         else:
             print("subtype not supported in HWY")
diff --git a/pelicun/resources/auto/Hazus_Earthquake_Story.py b/pelicun/resources/auto/Hazus_Earthquake_Story.py
index 0b2bd34eb..76fba65d7 100644
--- a/pelicun/resources/auto/Hazus_Earthquake_Story.py
+++ b/pelicun/resources/auto/Hazus_Earthquake_Story.py
@@ -264,6 +264,11 @@ def auto_populate(AIM):
             "Damage": {"DamageProcess": "Hazus Earthquake"},
             "Demands": {},
             "Losses": {"Repair": repair_config},
+            "Options": {
+                "NonDirectionalMultipliers": {
+                    "ALL": 1.0
+                },
+            }
         }
 
     else:
diff --git a/pelicun/uq.py b/pelicun/uq.py
index 67b51a66b..587d193ce 100644
--- a/pelicun/uq.py
+++ b/pelicun/uq.py
@@ -1245,9 +1245,9 @@ def __init__(
 
         """
         super().__init__(
-            name,
-            f_map,
-            anchor,
+            name=name,
+            f_map=f_map,
+            anchor=anchor,
         )
 
     @abstractmethod
@@ -1281,9 +1281,7 @@ class SampleSizeRandomVariable(BaseRandomVariable):
     @abstractmethod
     def __init__(
         self,
-        name,
-        theta,
-        truncation_limits=np.array((np.nan, np.nan)),
+        name,        
         f_map=None,
         anchor=None,
     ):
@@ -1293,15 +1291,7 @@ def __init__(
         Parameters
         ----------
         name: string
-            A unique string that identifies the random variable.
-        theta: 2-element float ndarray
-          Set of parameters that define the Cumulative Distribution
-          Function (CDF) of the variable: Mean, coefficient of
-          variation.
-        truncation_limits: float ndarray, optional
-          Defines the np.array((a, b)) truncation limits for the
-          distribution. Use np.nan to assign no limit in one direction,
-          like so: np.array((a, np.nan)), or np.array((np.nan, b)).
+            A unique string that identifies the random variable.        
         f_map: function, optional
             A user-defined function that is applied on the realizations before
             returning a sample.
@@ -1313,9 +1303,9 @@ def __init__(
 
         """
         super().__init__(
-            name,
-            f_map,
-            anchor,
+            name=name,
+            f_map=f_map,
+            anchor=anchor,
         )
 
     @abstractmethod
@@ -1349,9 +1339,11 @@ def __init__(
         anchor=None,
     ):
         super().__init__(
-            name,
-            f_map,
-            anchor,
+            name=name,
+            theta=theta,
+            truncation_limits=truncation_limits,
+            f_map=f_map,
+            anchor=anchor,
         )
         self.distribution = 'normal'
         self.theta = np.atleast_1d(theta)
@@ -1469,9 +1461,11 @@ def __init__(
         anchor=None,
     ):
         super().__init__(
-            name,
-            f_map,
-            anchor,
+            name=name,
+            theta=theta,
+            truncation_limits=truncation_limits,
+            f_map=f_map,
+            anchor=anchor,
         )
         self.distribution = 'lognormal'
         self.theta = np.atleast_1d(theta)
@@ -1583,9 +1577,11 @@ def __init__(
         anchor=None,
     ):
         super().__init__(
-            name,
-            f_map,
-            anchor,
+            name=name,
+            theta=theta,
+            truncation_limits=truncation_limits,
+            f_map=f_map,
+            anchor=anchor,
         )
         self.distribution = 'uniform'
         self.theta = np.atleast_1d(theta)
@@ -1670,9 +1666,11 @@ def __init__(
         anchor=None,
     ):
         super().__init__(
-            name,
-            f_map,
-            anchor,
+            name=name,
+            theta=theta,
+            truncation_limits=truncation_limits,
+            f_map=f_map,
+            anchor=anchor,
         )
         self.distribution = 'multilinear_CDF'
 
@@ -1791,9 +1789,11 @@ def __init__(
         anchor=None,
     ):
         super().__init__(
-            name,
-            f_map,
-            anchor,
+            name=name, 
+            theta=raw_samples,
+            truncation_limits=truncation_limits,           
+            f_map=f_map,
+            anchor=anchor,
         )
         self.distribution = 'empirical'
         if not np.all(np.isnan(truncation_limits)):
@@ -1871,9 +1871,9 @@ def __init__(
 
         """
         super().__init__(
-            name,
-            f_map,
-            anchor,
+            name=name,
+            f_map=f_map,
+            anchor=anchor,
         )
         self.distribution = 'coupled_empirical'
         if not np.all(np.isnan(truncation_limits)):
@@ -1957,9 +1957,9 @@ def __init__(
 
         """
         super().__init__(
-            name,
-            f_map,
-            anchor,
+            name=name,
+            f_map=f_map,
+            anchor=anchor,
         )
         self.distribution = 'deterministic'
         if not np.all(np.isnan(truncation_limits)):
@@ -2004,9 +2004,11 @@ def __init__(
         anchor=None,
     ):
         super().__init__(
-            name,
-            f_map,
-            anchor,
+            name=name,
+            theta=theta,
+            truncation_limits=truncation_limits,
+            f_map=f_map,
+            anchor=anchor,
         )
         if not np.all(np.isnan(truncation_limits)):
             raise NotImplementedError(
diff --git a/setup.py b/setup.py
index 516d89dba..88b3001d3 100644
--- a/setup.py
+++ b/setup.py
@@ -45,7 +45,7 @@ def read(*filenames, **kwargs):
         'numpy>=1.22.0, <2.0',
         'scipy>=1.7.0, <2.0',
         'pandas>=1.4.0, <3.0',
-        'tables>=3.7.0',
+        #'tables>=3.7.0',
     ],
     classifiers=[
         'Programming Language :: Python',