read the docs

.readthedocs.yaml

@@ -0,0 +1,13 @@
+version: 2
+
+build:
+  os: "ubuntu-22.04"
+  tools:
+    python: "3.10"
+
+python:
+  install:
+    - requirements: docs/requirements.txt
+
+sphinx:
+  configuration: docs/source/conf.py

README.md

@@ -7,7 +7,7 @@
         <img src="https://img.shields.io/badge/license-MIT-brightgreen.svg"
             alt="MIT license"></a> &nbsp;
     <a href="https://github.com/tfm000/sklarpy/actions/workflows/tests.yml">
-        <img src="https://github.com/tfm000/sklarpy/actions/workflows/tests.yml/badge.svg"
+        <img src="https://github.com/tfm000/sklarpy/actions/workflows/tests.yml/badge.svg?branch=main"
             alt="build"></a> &nbsp;
     <a href="https://pepy.tech/project/sklarpy">
         <img src="https://static.pepy.tech/personalized-badge/sklarpy?period=total&units=international_system&left_color=black&right_color=orange&left_text=Downloads"
@@ -29,9 +29,11 @@
 
 SklarPy (pronounced 'Sky-Lar-Pee' or 'Sky-La-Pie') is an open-source software for probability distribution fitting.
 It contains useful tools for fitting Copula, Multivariate and Univariate probability distributions.
-In addition to over 100 univariate distributions, we implement many multivariate normal mixture distributions and their copulas, including Gaussian, Student-T, Skewed-T and Generalized Hyperbolic distributions and copulas.
+In addition to over 100 univariate distributions, we implement many multivariate normal mixture distributions and their copulas, including Gaussian, Student-T, Skewed-T and Generalized Hyperbolic distributions.
 Named after Sklar's theorem and Abe Sklar, the American mathematician who proved that multivariate cumulative distribution functions can be expressed in terms of copulas and their marginals.
 
+This library has many different possible use cases, ranging from machine learning to finance.
+
 
 ## Table of contents
 
@@ -101,6 +103,7 @@ Further examples can be found <a href="https://github.com/tfm000/sklarpy/tree/ma
 - We allow for easy plotting of all our distributions, allowing you to visualize your models.
 - We use scipy.stats as a backend for all our univariate models, meaning as scipy expands and improves their model selection, so will ours!
 - We provide multivariate and univariate distributions, in addition to our copula models, meaning SklarPy can act as a one-stop-shop for all probability distribution fitting. A full list of our implemented multivariate distributions can be found <a href="https://github.com/tfm000/sklarpy/tree/main/sklarpy/multivariate"> here</a>.
+- We are continuing to expand our library, and are open to suggestions for new models to implement. If you have a model you would like to see implemented, please open an issue on our <a href="https://github.com/tfm000/sklarpy/issues">GitHub page</a>.
 
 ## Testing
 All tests are written using pytest and cover all user accessible code.

docs/Makefile

@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = source
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/make.bat

@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd

docs/requirements.txt

@@ -0,0 +1,2 @@
+sphinx==7.1.2
+sphinx-rtd-theme==1.3.0rc1

docs/source/Copulas.rst

@@ -0,0 +1,5 @@
+.. _copulas:
+
+##############
+Copula Models
+##############

docs/source/Installation.rst

@@ -0,0 +1,19 @@
+.. _installation:
+
+Installation
+------------
+
+To use SklarPy, first install it using pip:
+
+.. code-block:: text
+
+    pip install sklarpy
+
+For Developers
+--------------
+
+If you wish to add your own significant modifications to SklarPy, you can can clone the current repository using:
+
+.. code-block:: text
+
+        git clone https://github.com/tfm000/sklarpy

docs/source/Misc.rst

@@ -0,0 +1,5 @@
+.. _misc:
+
+####################
+Miscellaneous Tools
+####################

docs/source/Multivariate.rst

@@ -0,0 +1,5 @@
+.. _multivariate:
+
+############################
+Multivariate Distributions
+############################

docs/source/Univariate.rst

@@ -0,0 +1,6 @@
+.. _univariate:
+
+#########################
+Univariate Distributions
+#########################
+

docs/source/conf.py

@@ -0,0 +1,39 @@
+# Configuration file for the Sphinx documentation builder.
+import os
+import sys
+
+sys.path.insert(0, os.path.abspath("..."))
+
+# -- Project information
+
+project = 'SklarPy'
+copyright = '2023, Tyler Mitchell'
+author = 'Tyler Mitchell'
+
+release = '1.0.0'
+version = '1.0.0'
+
+# -- General configuration
+
+extensions = [
+    'sphinx.ext.duration',
+    'sphinx.ext.doctest',
+    'sphinx.ext.autodoc',
+    'sphinx.ext.autosummary',
+    'sphinx.ext.intersphinx',
+]
+
+intersphinx_mapping = {
+    'python': ('https://docs.python.org/3/', None),
+    'sphinx': ('https://www.sphinx-doc.org/en/master/', None),
+}
+intersphinx_disabled_domains = ['std']
+
+templates_path = ['_templates']
+
+# -- Options for HTML output
+
+html_theme = 'sphinx_rtd_theme'
+
+# -- Options for EPUB output
+epub_show_urls = 'footnote'

docs/source/index.rst

@@ -0,0 +1,117 @@
+.. image:: https://github.com/tfm000/sklarpy/blob/main/media/logo.png?raw=true
+   :alt: SklarPy logo
+   :scale: 60%
+   :align: center
+
+.. raw:: html
+
+    <meta name="description" content="Copula, Multivariate and Univariate probability distribution fitting in Python.">
+    <meta name="keywords" content="SklarPy, Sklar, Copula, Copulas, Copulae, Probability, Distribution, Univariate, Bivariate, Multivariate, Joint, CDF, PDF, Modeling, Quantitative, Fitting, Statistics, Mathematics, Finance, Risk, VaR, Variables">
+    <meta name="author" content="Tyler Mitchell">
+
+    <embed>
+        <p align="center">
+            <a href="https://github.com/tfm000/sklarpy/blob/main/LICENSE">
+                <img src="https://img.shields.io/badge/license-MIT-brightgreen.svg"
+                    alt="MIT license"></a> &nbsp;
+            <a href="https://github.com/tfm000/sklarpy/actions/workflows/tests.yml">
+                <img src="https://github.com/tfm000/sklarpy/actions/workflows/tests.yml/badge.svg"
+                    alt="build"></a> &nbsp;
+            <a href="https://pepy.tech/project/sklarpy">
+                <img src="https://static.pepy.tech/personalized-badge/sklarpy?period=total&units=international_system&left_color=black&right_color=orange&left_text=Downloads"
+                    alt="downloads"></a> &nbsp;
+            <a href="https://pypi.org/project/sklarpy/">
+                <img src="https://img.shields.io/badge/Maintained%3F-yes-green.svg"
+                    alt="maintained"></a>
+        </p>
+
+        <p align="center">
+            <a href="https://pypi.org/project/sklarpy/">
+                <img src="https://img.shields.io/badge/mac%20os-000000?style=for-the-badge&logo=apple&logoColor=white"
+                    alt="mac os"></a>
+            <a href="https://pypi.org/project/sklarpy/">
+                <img src="https://img.shields.io/badge/Windows-0078D6?style=for-the-badge&logo=windows&logoColor=white"
+                    alt="windows"></a>
+        </p>
+    </embed>
+
+SklarPy (pronounced 'Sky-Lar-Pee' or 'Sky-La-Pie') is an open-source software for probability distribution fitting.
+It contains useful tools for fitting Copula, Multivariate and Univariate probability distributions.
+In addition to over 100 univariate distributions, we implement many multivariate normal mixture distributions and their copulas, including Gaussian, Student-T, Skewed-T and Generalized Hyperbolic distributions.
+Named after Sklar's theorem and Abe Sklar, the American mathematician who proved that multivariate cumulative distribution functions can be expressed in terms of copulas and their marginals.
+
+This library has many different possible use cases, ranging from machine learning to finance.
+
+Contents
+--------
+
+.. toctree::
+    :maxdepth: 2
+   Installation
+   Univariate
+   Multivariate
+   Copulas
+   Misc
+
+Why we are better
+-----------------
+- Unlike other Python implementations of copulas, we implement more than the Gaussian and Archimedean copulas. A full list of our implementated copula models can be found in the documentation, though it includes many normal mean-variance mixture models as well as Archimedean and non-parametric models.
+- We allow for easy parameter fitting of both the univariate marginals and the multivariate copula distribution.
+- We allow for easy plotting of all our distributions, allowing you to visualize your models.
+- We use scipy.stats as a backend for all our univariate models, meaning as scipy expands and improves their model selection, so will ours!
+- We provide multivariate and univariate distributions, in addition to our copula models, meaning SklarPy can act as a one-stop-shop for all probability distribution fitting. A full list of our implemented multivariate distributions can be found in the documentation.
+- We are continuing to expand our library, and are open to suggestions for new models to implement. If you have a model you would like to see implemented, please open an issue on our GitHub page.
+
+Example
+--------
+
+Here we show a quick example of working with SklarPy.
+For more information, see the specific documentaion.::
+
+    import numpy as np
+    import pandas as pd
+    import matplotlib.pyplot as plt
+    from sklarpy.copulas import gh_copula
+
+    # generating random data
+    n: int = 1000
+    obs: np.ndarray = np.full((n, 2), np.nan)
+    obs[:, 0] = np.random.normal(3,4, size=(n,))
+    obs[:, 1] = obs[:, 0] + 0.5 * np.random.normal(3, 5, size=(n,))
+    obvs_df: pd.DataFrame = pd.DataFrame(obs, columns=['Process A', 'Process B'])
+
+    # fitting our copula model
+    fitted_copula = gh_copula.fit(obvs_df)
+
+    # printing our fitted copula parameters
+    print(fitted_copula.copula_params.to_dict)
+
+    # printing our fitted marginal distributions
+    print(fitted_copula.mdists)
+
+    # plotting our fit
+    fitted_copula.pdf_plot(show=False)
+    fitted_copula.copula_pdf_plot(show=False)
+    plt.show()
+
+This outputs:
+
+.. code-block:: text
+
+    {'lamb': -10.0, 'chi': 4.227038325195731, 'psi': 10.0,
+        'loc': array([[0.], [0.]]),
+        'shape': array([[1. , 0.84273015],
+                        [0.84273015, 1.]]),
+        'gamma': array([[0.99696041], [0.99913161]])}
+
+    {0: lognorm(0.02, -203.22, 206.18), 1: lognorm(0.04, -110.89, 115.4)}
+
+.. image:: https://github.com/tfm000/sklarpy/blob/main/media/PDF_Gh_PDF_Plot_Plot.png?raw=true
+   :alt: GH PDF
+   :scale: 60%
+   :align: center
+
+.. image:: https://github.com/tfm000/sklarpy/blob/main/media/Copula_PDF_Gh_Copula_PDF_Plot_Plot.png?raw=true
+   :alt: GH Copula PDF
+   :scale: 60%
+   :align: center

pyproject.toml

@@ -66,6 +66,9 @@ dev = [
     "pytest==7.1.2",
     "tox==3.25.1",
 ]
+docs = [
+    "sphinx~=4.2.0",
+]
 
 [project.urls]  # Optional
 "Homepage" = "https://github.com/tfm000/sklarpy"