<h1>Python</h1>
<p>Although this is not a statistical package <em>per se,</em> it has extensive statistical capabilities.</p>
<ul>
<li><p><a href="https://matplotlib.org/" rel="nofollow noreferrer">Matplotlib</a> is a library for visualization. It has an assortment of <a href="https://matplotlib.org/stable/tutorials/index" rel="nofollow noreferrer">tutorials</a> and a <a href="https://matplotlib.org/stable/gallery/" rel="nofollow noreferrer">gallery of examples</a> in its documentation.</p>
</li>
<li><p>The <a href="http://wiki.python.org/moin/FrontPage" rel="nofollow noreferrer">Python Wiki</a> is a gateway to Python.</p>
</li>
<li><p><a href="https://www.python.org/community/forums/" rel="nofollow noreferrer">Python Forum</a> is a Python-oriented Q&amp;A site.</p>
</li>
<li><p>From Python 3.4, <a href="http://www.python.org/dev/peps/pep-0450/" rel="nofollow noreferrer">the &quot;statistics&quot; module</a> will be added to the standard library.</p>
</li>
<li><p><a href="https://pingouin-stats.org/" rel="nofollow noreferrer">Pingouin</a> is a <code>pandas</code>-aware library which provides diagnostic plots, power analysis, and various statistical tests.</p>
</li>
<li><p><a href="https://pypi.org/project/statpy/" rel="nofollow noreferrer">StatPy</a> (&quot;special emphasis on astrostatistics&quot;).</p>
</li>
<li><p>Links to <a href="http://wiki.python.org/moin/NumericAndScientific" rel="nofollow noreferrer">numerical and scientific</a> packages.</p>
</li>
<li><p><a href="http://scikit-learn.org/stable/" rel="nofollow noreferrer">Scikit-learn</a> for machine learning.</p>
</li>
<li><p><a href="https://docs.scipy.org/doc/scipy/reference/stats.html" rel="nofollow noreferrer">SciPy.stats</a> contains numerous classes for probability distributions and statistical tests. Other submodules of SciPy also provide numerical integration and integral transforms.</p>
</li>
<li><p><a href="https://arxiv.org/abs/2106.01140" rel="nofollow noreferrer">SemoPy2</a> provides trainable model classes for structural equation models. They support <a href="https://semopy.com/efa.html" rel="nofollow noreferrer">exploratory</a> and confirmatory approaches, <a href="https://semopy.com/mixed.html" rel="nofollow noreferrer">random effects</a>, <a href="https://semopy.com/factor_scores.html" rel="nofollow noreferrer">latent factor scores</a>, <a href="https://semopy.com/means.html" rel="nofollow noreferrer">mean components</a>, among other features.</p>
</li>
<li><p><a href="https://www.statsmodels.org/stable/index.html" rel="nofollow noreferrer">statsmodels</a>, for a variety of models including generalized linear models, Markov models, and autoregressive models. Support for <a href="https://www.statsmodels.org/dev/examples/notebooks/generated/robust_models_1.html" rel="nofollow noreferrer">M-estimation</a>, generalized least squares, maximum-likelihood, and seemingly-unrelated regression are included.</p>
</li>
<li><p><a href="https://docs.sympy.org/latest/modules/stats.html" rel="nofollow noreferrer">SymPy.stats</a> provides a basic computer algebra system for symbolic statistical computing. The main module also provides a variety of mathematical tools such as derivatives, integrals (including Fourier transforms), and other tools that are useful in mathematical statistics.</p>
</li>
<li><p><a href="http://pandas.pydata.org/" rel="nofollow noreferrer">pandas</a>, for enhanced data structures, including time-series.</p>
</li>
</ul>
<p>In addition, people who prefer Python for scripting, but would like access to <code>R</code>'s wide-ranging statistical capabilities can call <code>R</code> from Python with <a href="http://rpy.sourceforge.net/rpy2/doc-dev/html/introduction.html#getting-started" rel="nofollow noreferrer">rpy2</a> (see also: <a href="https://web.archive.org/web/20200412094634/https://sites.google.com/site/aslugsguidetopython/data-analysis/pandas/calling-r-from-python" rel="nofollow noreferrer">A Slug's Guide to Python</a> (archived)).</p>
