From e742ac9abe68e190ed670de7ac4e3969c4803bdb Mon Sep 17 00:00:00 2001 From: Raktim Mukhopadhyay Date: Wed, 4 Sep 2024 14:43:37 -0400 Subject: [PATCH] updated docs --- README.rst | 2 +- doc/source/conf.py | 16 ++++++++++++++++ 2 files changed, 17 insertions(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 36b086b..6276fb0 100644 --- a/README.rst +++ b/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 `_. For more details please see `Golzy and Markatou (2020) `_. diff --git a/doc/source/conf.py b/doc/source/conf.py index 4cc0ad9..d43b28d 100644 --- a/doc/source/conf.py +++ b/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 = []