[ENH] Log Laplace Distribution (#374)

Towards #22, Implements Log Laplace Distribution

docs/source/api_reference/distributions.rst

@@ -45,6 +45,7 @@ Continuous support
     HalfNormal
     Laplace
     Logistic
+    LogLaplace
     Normal
     TDistribution
     Weibull

skpro/distributions/__init__.py

@@ -17,6 +17,7 @@
     "IID",
     "Laplace",
     "Logistic",
+    "LogLaplace",
     "LogNormal",
     "Mixture",
     "Normal",
@@ -44,6 +45,7 @@
 from skpro.distributions.halfnormal import HalfNormal
 from skpro.distributions.laplace import Laplace
 from skpro.distributions.logistic import Logistic
+from skpro.distributions.loglaplace import LogLaplace
 from skpro.distributions.lognormal import LogNormal
 from skpro.distributions.mixture import Mixture
 from skpro.distributions.normal import Normal

skpro/distributions/loglaplace.py

@@ -0,0 +1,79 @@
+# copyright: skpro developers, BSD-3-Clause License (see LICENSE file)
+"""Log-Laplace probability distribution."""
+
+__author__ = ["SaiRevanth25"]
+
+import pandas as pd
+from scipy.stats import loglaplace, rv_continuous
+
+from skpro.distributions.adapters.scipy import _ScipyAdapter
+
+
+class LogLaplace(_ScipyAdapter):
+    r"""Log-Laplace distribution.
+
+    This distribution is univariate, without correlation between dimensions
+    for the array-valued case.
+
+    The log-Laplace distribution is a continuous probability distribution obtained by
+    taking the logarithm of the Laplace distribution, commonly used in finance and
+    hydrology due to its heavy tails and asymmetry.
+
+    The log-Laplace distribution is parametrized by the scale parameter
+    :math:`\c`, such that the pdf is
+
+    .. math:: f(x) = \frac{c}{2} x^{c-1}, \quad 0<x<1
+
+    and
+
+    .. math:: f(x) = \frac{c}{2} x^{-c-1}, \quad x >= 1
+
+    The scale parameter :math:`c` is represented by the parameter ``c``.
+
+    Parameters
+    ----------
+    scale : float or array of float (1D or 2D), must be positive
+        scale parameter of the log-Laplace distribution
+    index : pd.Index, optional, default = RangeIndex
+    columns : pd.Index, optional, default = RangeIndex
+
+    Example
+    -------
+    >>> from skpro.distributions.loglaplace import LogLaplace
+
+    >>> ll = LogLaplace(scale=1)
+    """
+
+    _tags = {
+        "capabilities:approx": ["pdfnorm"],
+        "capabilities:exact": ["mean", "var", "pdf", "log_pdf", "cdf", "ppf"],
+        "distr:measuretype": "continuous",
+        "distr:paramtype": "parametric",
+        "broadcast_init": "on",
+    }
+
+    def __init__(self, scale, index=None, columns=None):
+        self.scale = scale
+
+        super().__init__(index=index, columns=columns)
+
+    def _get_scipy_object(self) -> rv_continuous:
+        return loglaplace
+
+    def _get_scipy_param(self):
+        scale = self._bc_params["scale"]
+        return [scale], {}
+
+    @classmethod
+    def get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the estimator."""
+        # array case examples
+        params1 = {"scale": [[1, 2], [3, 4]]}
+        params2 = {
+            "scale": 1,
+            "index": pd.Index([1, 2, 5]),
+            "columns": pd.Index(["a", "b"]),
+        }
+        # scalar case examples
+        params3 = {"scale": 2}
+        return [params1, params2, params3]