Backwards-compatible rename Lognormal→LogNormal

Author: michaelosthege

RELEASE-NOTES.md

@@ -5,6 +5,7 @@
 + The `pm.logp(rv, x)` syntax is now available and recommended to make your model code `v4`-ready. Note that this backport is just an alias and much less capable than what's available with `pymc >=4` (see [#5083](https://github.com/pymc-devs/pymc/pulls/5083)).
 + The `pm.Distribution(testval=...)` kwarg was deprecated and will be replaced by `pm.Distribution(initval=...)`in `pymc >=4` (see [#5226](https://github.com/pymc-devs/pymc/pulls/5226)).
 + The `pm.sample(start=...)` kwarg was deprecated and will be replaced by `pm.sample(initvals=...)`in `pymc >=4` (see [#5226](https://github.com/pymc-devs/pymc/pulls/5226)).
++ `pm.Lognormal` is now available as an alias for `pm.Lognormal` (see [#5389](https://github.com/pymc-devs/pymc/pull/5389)).
 
 ### Bugfixes
 + A hotfix is applied on import to remain compatible with NumPy 1.22 (see [#5316](https://github.com/pymc-devs/pymc/pull/5316)).

pymc3/distributions/__init__.py

@@ -35,7 +35,7 @@
     Laplace,
     Logistic,
     LogitNormal,
-    Lognormal,
+    LogNormal,
     Moyal,
     Normal,
     Pareto,
@@ -119,7 +119,7 @@
     "Gamma",
     "Weibull",
     "Bound",
-    "Lognormal",
+    "LogNormal",
     "HalfStudentT",
     "ChiSquared",
     "HalfNormal",

pymc3/distributions/continuous.py

@@ -62,7 +62,7 @@
     "Gamma",
     "Weibull",
     "HalfStudentT",
-    "Lognormal",
+    "LogNormal",
     "ChiSquared",
     "HalfNormal",
     "Wald",
@@ -1778,7 +1778,7 @@ def logp(self, value):
         )
 
 
-class Lognormal(PositiveContinuous):
+class LogNormal(PositiveContinuous):
     r"""
     Log-normal log-likelihood.
 
@@ -1834,10 +1834,10 @@ class Lognormal(PositiveContinuous):
 
         # Example to show that we pass in only ``sigma`` or ``tau`` but not both.
         with pm.Model():
-            x = pm.Lognormal('x', mu=2, sigma=30)
+            x = pm.LogNormal('x', mu=2, sigma=30)
 
         with pm.Model():
-            x = pm.Lognormal('x', mu=2, tau=1/100)
+            x = pm.LogNormal('x', mu=2, tau=1/100)
     """
 
     def __init__(self, mu=0, sigma=None, tau=None, sd=None, *args, **kwargs):
@@ -1856,16 +1856,16 @@ def __init__(self, mu=0, sigma=None, tau=None, sd=None, *args, **kwargs):
         self.mode = tt.exp(self.mu - 1.0 / self.tau)
         self.variance = (tt.exp(1.0 / self.tau) - 1) * tt.exp(2 * self.mu + 1.0 / self.tau)
 
-        assert_negative_support(tau, "tau", "Lognormal")
-        assert_negative_support(sigma, "sigma", "Lognormal")
+        assert_negative_support(tau, "tau", "LogNormal")
+        assert_negative_support(sigma, "sigma", "LogNormal")
 
     def _random(self, mu, tau, size=None):
         samples = np.random.normal(size=size)
         return np.exp(mu + (tau ** -0.5) * samples)
 
     def random(self, point=None, size=None):
         """
-        Draw random values from Lognormal distribution.
+        Draw random values from LogNormal distribution.
 
         Parameters
         ----------
@@ -1885,7 +1885,7 @@ def random(self, point=None, size=None):
 
     def logp(self, value):
         """
-        Calculate log-probability of Lognormal distribution at specified value.
+        Calculate log-probability of LogNormal distribution at specified value.
 
         Parameters
         ----------
@@ -1911,7 +1911,7 @@ def _distr_parameters_for_repr(self):
 
     def logcdf(self, value):
         """
-        Compute the log of the cumulative distribution function for Lognormal distribution
+        Compute the log of the cumulative distribution function for LogNormal distribution
         at the specified value.
 
         Parameters
@@ -1935,6 +1935,9 @@ def logcdf(self, value):
         )
 
 
+Lognormal = LogNormal
+
+
 class StudentT(Continuous):
     r"""
     Student's T log-likelihood.