updated docs

README.rst

@@ -75,7 +75,7 @@ Introduction
 
 The QuadratiK package is implemented in both **R** and **Python**, providing a comprehensive set of goodness-of-fit tests and a clustering technique using kernel-based quadratic distances. This framework aims to bridge the gap between the statistical and machine learning literatures. It includes:
 
-* **Goodness-of-Fit Tests** : The software implements one, two, and k-sample tests for goodness of fit, offering an efficient and mathematically sound way to assess the fit of probability distributions. Expanded capabilities include supporting tests for uniformity on the $d$-dimensional Sphere based on Poisson kernel densities. Our tests are particularly useful for large, high-dimensional datasets where the assessment of fit of probability models is of interest. Specifically, we offer tests for normality, as well as two- and k-sample tests, where testing equality of two or more distributions is of interest, i.e. $H_0: F_1 = F_2$ and $H_0: F_1 = \\ldots = F_k$ respectively. The proposed tests perform well in terms of level and power for contiguous alternatives, heavy tailed distributions and in higher dimensions. 
+* **Goodness-of-Fit Tests** : The software implements one, two, and k-sample tests for goodness of fit, offering an efficient and mathematically sound way to assess the fit of probability distributions. Expanded capabilities include supporting tests for uniformity on the :math:`d`-dimensional Sphere based on Poisson kernel densities. Our tests are particularly useful for large, high-dimensional datasets where the assessment of fit of probability models is of interest. Specifically, we offer tests for normality, as well as two- and k-sample tests, where testing equality of two or more distributions is of interest, i.e. :math:`H_0: F_1 = F_2` and :math:`H_0: F_1 = \ldots = F_k` respectively. The proposed tests perform well in terms of level and power for contiguous alternatives, heavy tailed distributions and in higher dimensions. 
 
 * **Poisson Kernel-based Distribution (PKBD)** :  The package also includes functionality for generating random samples from PKBD and computing the density value. A short guide on PKBD is included in `User Guide <user_guide>`_. For more details please see `Golzy and Markatou (2020) <https://www.tandfonline.com/doi/abs/10.1080/10618600.2020.1740713>`_.
 

doc/source/conf.py

@@ -36,6 +36,22 @@
     "myst_parser",
 ]
 
+myst_enable_extensions = [
+    "amsmath",
+    "attrs_inline",
+    "colon_fence",
+    "deflist",
+    "dollarmath",
+    "fieldlist",
+    "html_admonition",
+    "html_image",
+    "replacements",
+    "smartquotes",
+    "strikethrough",
+    "substitution",
+    "tasklist",
+]
+
 templates_path = ["_templates"]
 exclude_patterns = []