ensures that tests pass

src/hssm/hssm.py

@@ -165,12 +165,6 @@ class HSSM:
         recommended when you are using hierarchical models.
         The default value is `None` when `hierarchical` is `False` and `"safe"` when
         `hierarchical` is `True`.
-    center_predictors : optional
-        If `True`, and if there is an intercept in the common terms, the
-        data is centered by subtracting the mean. The centering is undone after sampling
-        to provide the actual intercept in all distributional components that have an
-        intercept. Note that this changes the interpretation of the prior on the
-        intercept because it refers to the intercept of the centered data.
     extra_namespace : optional
         Additional user supplied variables with transformations or data to include in
         the environment where the formula is evaluated. Defaults to `None`.
@@ -224,7 +218,6 @@ def __init__(
         hierarchical: bool = False,
         link_settings: Literal["log_logit"] | None = None,
         prior_settings: Literal["safe"] | None = None,
-        center_predictors: bool = False,
         extra_namespace: dict[str, Any] | None = None,
         **kwargs,
     ):
@@ -333,7 +326,6 @@ def __init__(
             data=data,
             family=self.family,
             priors=self.priors,
-            center_predictors=center_predictors,
             extra_namespace=extra_namespace,
             **other_kwargs,
         )
@@ -938,7 +930,7 @@ def _preprocess_rest(self, processed: dict[str, Param]) -> dict[str, Param]:
                     bounds = self.model_config.bounds.get(param_str)
                     param = Param(
                         param_str,
-                        formula="1 + (1|participant_id)",
+                        formula=f"{param_str} ~ 1 + (1|participant_id)",
                         bounds=bounds,
                     )
                 else:
@@ -982,8 +974,8 @@ def _find_parent(self) -> tuple[str, Param]:
     def _override_defaults(self):
         """Override the default priors or links."""
         is_ddm = (
-            self.model in ["ddm", "ddm_sdv"] and self.loglik_kind == "analytical"
-        ) or (self.model == "ddm_full" and self.loglik_kind == "blackbox")
+            self.model_name in ["ddm", "ddm_sdv"] and self.loglik_kind == "analytical"
+        ) or (self.model_name == "ddm_full" and self.loglik_kind == "blackbox")
         for param in self.list_params:
             param_obj = self.params[param]
             if self.prior_settings == "safe":

src/hssm/param.py

@@ -1,12 +1,12 @@
 """The Param utility class."""
 
 import logging
-from typing import Any, Union, cast
+from copy import deepcopy
+from typing import Any, Literal, Union, cast
 
 import bambi as bmb
 import numpy as np
 import pandas as pd
-from deepcopy import deepcopy
 from formulae import design_matrices
 
 from .link import Link
@@ -100,7 +100,7 @@ def override_default_link(self):
 
         This is most likely because both default prior and default bounds are supplied.
         """
-        self._ensure_not_converted()
+        self._ensure_not_converted(context="link")
 
         if not self.is_regression or self._link_specified:
             return  # do nothing
@@ -144,7 +144,7 @@ def override_default_priors(self, data: pd.DataFrame, eval_env: dict[str, Any]):
         eval_env
             The environment used to evaluate the formula.
         """
-        self._ensure_not_converted()
+        self._ensure_not_converted(context="prior")
 
         if not self.is_regression:
             return
@@ -195,7 +195,7 @@ def override_default_priors_ddm(self, data: pd.DataFrame, eval_env: dict[str, An
         eval_env
             The environment used to evaluate the formula.
         """
-        self._ensure_not_converted()
+        self._ensure_not_converted(context="prior")
         assert self.name is not None
 
         if not self.is_regression:
@@ -238,7 +238,7 @@ def override_default_priors_ddm(self, data: pd.DataFrame, eval_env: dict[str, An
             prior = cast(dict[str, ParamSpec], self.prior)
             self.prior = merge_dicts(override_priors, prior)
 
-    def _get_design_matrices(self, data: pd.DataFrame, eval_env: dict[str, Any]):
+    def _get_design_matrices(self, data: pd.DataFrame, extra_namespace: dict[str, Any]):
         """Get the design matrices for the regression.
 
         Parameters
@@ -251,19 +251,17 @@ def _get_design_matrices(self, data: pd.DataFrame, eval_env: dict[str, Any]):
         formula = cast(str, self.formula)
         rhs = formula.split("~")[1]
         formula = "rt ~ " + rhs
-        dm = design_matrices(formula, data=data, eval_env=eval_env)
+        dm = design_matrices(formula, data=data, extra_namespace=extra_namespace)
 
         return dm
 
-    def _ensure_not_converted(self):
+    def _ensure_not_converted(self, context=Literal["link", "prior"]):
         """Ensure that the object has not been converted."""
         if self._is_converted:
+            context = "link function" if context == "link" else "priors"
             raise ValueError(
-                (
-                    "Cannot override the default priors for parameter %s."
-                    + " The object has already been processed."
-                )
-                % self.name,
+                f"Cannot override the default {context} for parameter {self.name}."
+                + " The object has already been processed."
             )
 
     def set_parent(self):

src/hssm/prior.py

@@ -146,7 +146,9 @@ def TruncatedDist(name):
 
 
 def generate_prior(
-    dist: str | dict | int | float, bounds: tuple[float, float] | None = None, **kwargs
+    dist: str | dict | int | float | Prior,
+    bounds: tuple[float, float] | None = None,
+    **kwargs,
 ):
     """Generate a Prior distribution.
 
@@ -184,10 +186,12 @@ def generate_prior(
         prior: Prior | int | float = Prior(dist, bounds=bounds, **default_settings)
     elif isinstance(dist, dict):
         prior_settings = dist.copy()
-        dist = prior_settings.pop("dist")
+        dist_name: str = prior_settings.pop("dist")
         for k, v in prior_settings.items():
             prior_settings[k] = generate_prior(v)
-        prior = generate_prior(dist, bounds=bounds, **prior_settings)
+        prior = Prior(dist_name, bounds=bounds, **prior_settings)
+    elif isinstance(dist, Prior):
+        prior = dist
     elif isinstance(dist, (int, float)):
         if bounds is not None:
             lower, upper = bounds
@@ -246,9 +250,9 @@ def get_default_prior(term_type: str, bounds: tuple[float, float] | None):
         else:
             prior = generate_prior("Normal")
     elif term_type == "group_intercept":
-        prior = generate_prior("Normal", mu="Normal", sigma="Weibull", bounds=bounds)
+        prior = generate_prior("Normal", mu="Normal", sigma="Weibull")
     elif term_type == "group_specific":
-        prior = generate_prior("Normal", mu="Normal", sigma="Weibull", bounds=None)
+        prior = generate_prior("Normal", mu="Normal", sigma="Weibull")
     else:
         raise ValueError("Unrecognized term type.")
     return prior
@@ -263,7 +267,7 @@ def get_hddm_default_prior(
     elif term_type == "common_intercept":
         prior = generate_prior(HDDM_MU[param], bounds=bounds)
     elif term_type == "group_intercept":
-        prior = generate_prior(HDDM_SETTINGS_GROUP[param], bounds=bounds)
+        prior = generate_prior(HDDM_SETTINGS_GROUP[param], bounds=None)
     elif term_type == "group_specific":
         prior = generate_prior("Normal", mu="Normal", sigma="Weibull", bounds=None)
     else:
@@ -274,6 +278,9 @@ def get_hddm_default_prior(
 HSSM_SETTINGS_DISTRIBUTIONS: dict[Any, Any] = {
     "Normal": {"mu": 0.0, "sigma": 0.25},
     "Weibull": {"alpha": 1.5, "beta": 0.3},
+    "HalfNormal": {"sigma": 0.25},
+    "Beta": {"alpha": 1.0, "beta": 1.0},
+    "Gamma": {"k": 1.0, "theta": 1.0},
 }
 
 HDDM_MU: dict[Any, Any] = {