Add ChiSquared distribution

preliz/distributions/__init__.py

@@ -5,6 +5,7 @@
     Beta,
     BetaScaled,
     Cauchy,
+    ChiSquared,
     Exponential,
     Gamma,
     HalfCauchy,

preliz/distributions/continuous.py

@@ -310,6 +310,71 @@ def _fit_mle(self, sample, **kwargs):
         self._update(alpha, beta)
 
 
+class ChiSquared(Continuous):
+    r"""
+    Chi squared  log-likelihood.
+
+    The pdf of this distribution is
+
+    .. math::
+
+       f(x \mid \nu) =
+                \frac{x^{(\nu-2)/2}e^{-x/2}}{2^{\nu/2}\Gamma(\nu/2)}
+
+    .. plot::
+        :context: close-figs
+
+        import arviz as az
+        from preliz import ChiSquared
+        az.style.use('arviz-white')
+        nus = [1., 3., 9.]
+        for nu in nus:
+                ax = ChiSquared(nu).plot_pdf(support=(0,20))
+                ax.set_ylim(0, 0.6)
+
+    ========  ===============================
+    Support   :math:`x \in [0, \infty)`
+    Mean      :math:`\nu`
+    Variance  :math:`2 \nu`
+    ========  ===============================
+
+    Parameters
+    ----------
+    nu : float
+        Degrees of freedom (nu > 0).
+    """
+
+    def __init__(self, nu=None):
+        super().__init__()
+        self.nu = nu
+        self.name = "chisquared"
+        self.params = (self.nu,)
+        self.param_names = ("nu",)
+        self.params_support = ((eps, np.inf),)
+        self.dist = stats.chi2
+        self.support = (0, np.inf)
+        self._update_rv_frozen()
+
+    def _get_frozen(self):
+        frozen = None
+        if any(self.params):
+            frozen = self.dist(self.nu)
+        return frozen
+
+    def _update(self, nu):
+        self.nu = nu
+        self.params = (self.nu,)
+        self._update_rv_frozen()
+
+    def _fit_moments(self, mean, sigma=None):  # pylint: disable=unused-argument
+        nu = mean
+        self._update(nu)
+
+    def _fit_mle(self, sample, **kwargs):
+        nu, _, _ = self.dist.fit(sample, **kwargs)
+        self._update(nu)
+
+
 class Exponential(Continuous):
     r"""
     Exponential Distribution

preliz/tests/test_distributions.py

@@ -5,6 +5,7 @@
 from preliz.distributions import (
     Beta,
     Cauchy,
+    ChiSquared,
     Gamma,
     Exponential,
     HalfCauchy,
@@ -33,6 +34,7 @@
     [
         (Normal, (0, 1)),
         (Beta, (2, 5)),
+        (ChiSquared, (1,)),
         (Gamma, (1, 0.5)),
         (HalfNormal, (1,)),
         (HalfStudent, (3, 1)),
@@ -80,6 +82,7 @@ def test_moments(distribution, params):
         (Normal, (0, 1)),
         (Beta, (2, 5)),
         (Cauchy, (0, 1)),
+        (ChiSquared, (1,)),
         (Gamma, (1, 0.5)),
         (HalfCauchy, (1,)),
         (HalfNormal, (1,)),

preliz/tests/test_maxent.py

@@ -14,6 +14,7 @@
 from preliz.distributions import (
     Beta,
     Cauchy,
+    ChiSquared,
     Exponential,
     Gamma,
     Laplace,
@@ -34,6 +35,7 @@
     [
         (Beta, "beta", 0.2, 0.6, 0.9, None, (0, 1), (6.112, 9.101)),
         (Cauchy, "cauchy", -1, 1, 0.6, None, (-np.inf, np.inf), (0, 0.726)),
+        (ChiSquared, "chisquared", 0, 4, 0.9, 1, (0, np.inf), (1.659)),
         (Exponential, "exponential", 0, 4, 0.9, None, (0, np.inf), (0.575)),
         (Gamma, "gamma", 0, 10, 0.7, None, (0, np.inf), (0.868, 0.103)),
         (HalfCauchy, "halfcauchy", 0, 10, 0.7, None, (0, np.inf), (5.095)),

preliz/tests/test_mle.py

@@ -10,6 +10,7 @@
         (pz.Beta, (2, 5)),
         (pz.BetaScaled, (2, 5, -1, 4)),
         (pz.Cauchy, (0, 1)),
+        (pz.ChiSquared, (1,)),
         (pz.Exponential, (5,)),
         (pz.Gamma, (2, 5)),
         (pz.HalfCauchy, (1,)),