Add to_bambi method

preliz/distributions/distributions.py

@@ -315,8 +315,6 @@ def to_pymc(self, name=None, **kwargs):
         -------
         PyMC distribution
         """
-        pymc_dist = None
-
         try:
             import pymc.distributions as pm_dists
             from pymc.model import Model
@@ -369,10 +367,29 @@ def to_pymc(self, name=None, **kwargs):
                 else:
                     pymc_dist = pymc_class(name, **self.params_dict, **kwargs)
 
+            return pymc_dist
+
         except ImportError:
-            pass
+            raise ImportError("This function requires PyMC") from None
+
+    def to_bambi(self, **kwargs):
+        """
+        Convert the PreliZ distribution to a Bambi Prior.
+
+        kwargs : PyMC distributions properties
+            kwargs are used to specify properties such as shape or dims
 
-        return pymc_dist
+        Returns
+        -------
+        Bambi Prior
+        """
+        try:
+            from bambi import Prior
+
+            return Prior(self.__class__.__name__, **self.params_dict, **kwargs)
+
+        except ImportError:
+            raise ImportError("This function requires Bambi") from None
 
     def _check_endpoints(self, lower, upper, raise_error=True):
         """

preliz/tests/test_distributions.py

@@ -313,3 +313,10 @@ def test_to_pymc():
     assert model.basic_RVs[2].ndim == 0
     assert Normal(0, 1).to_pymc(shape=2).ndim == 1
     assert Censored(Normal(0, 1), lower=0).to_pymc().ndim == 0
+
+
+def test_to_bambi():
+    bambi_prior = Gamma(mu=2, sigma=1).to_bambi()
+    assert bambi_prior.name == "Gamma"
+    assert bambi_prior.args["mu"] == 2
+    assert bambi_prior.args["sigma"] == 1