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<!DOCTYPE HTML>
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<title>JuliaStats.org</title>
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<span class="image image-full"><img src="images/logo.png" alt="JuliaStats Logo" /></span>
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<header>
<h1>JuliaStats</h1>
</header>
<p><strong>Statistics</strong> and <strong>Machine Learning</strong> made easy in Julia.</p>
<ul style="list-style-type:disc; margin-left:50px">
<li>Easy to use tools for statistics and machine learning.</li>
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<h2>Packages</h2>
<span>We bring together a number of great packages<br/>Use the <b><a href="https://github.com/JuliaStats/StatsKit.jl">StatsKit</a></b> meta-package to load all essential packages for statistics</span>
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<section class="box">
<h3><a href="https://github.com/JuliaStats/StatsBase.jl">StatsBase</a></h3>
<p><strong>Basic functionalities for statistics</strong></p>
<ul class="funlist">
<li>Descriptive statistics and moments</li>
<li>Sampling with/without replacement</li>
<li>Counting and ranking</li>
<li>Autocorrelation and cross-correlation</li>
<li>Weighted statistics</li>
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<section class="box">
<h3><a href="https://github.com/JuliaStats/StatsModels.jl">StatsModels</a></h3>
<p><strong>Interfaces for statistical models</strong></p>
<ul class="funlist">
<li>Formula and model frames</li>
<li>Essential functions for statistical models</li>
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<section class="box">
<h3><a href="https://github.com/JuliaStats/DataFrames.jl">DataFrames</a></h3>
<p><strong>Essential tools for tabular data</strong></p>
<ul class="funlist">
<li>DataFrames to represent tabular datasets</li>
<li>Database-style joins and indexing</li>
<li>Split-apply-combine operations, pivoting</li>
</ul>
</section>
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<section class="box">
<h3><a href="https://github.com/JuliaStats/Distributions.jl">Distributions</a></h3>
<p><strong>Probability distributions</strong></p>
<ul class="funlist">
<li>A large collection of univariate, multivariate distributions</li>
<li>descriptive stats, pdf/pmf, and mgf</li>
<li>Efficient sampling</li>
<li>Maximum likelihood estimation</li>
</ul>
</section>
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<div class="4u">
<section class="box">
<h3><a href="https://github.com/JuliaStats/MultivariateStats.jl">MultivariateStats</a></h3>
<p><strong>Multivariate statistical analysis</strong></p>
<ul class="funlist">
<li>Linear regression (LSQ and Ridge)</li>
<li>Dimensionality reduction (PCA,CCA,ICA,...)</li>
<li>Multidimensional scaling</li>
<li>Linear discriminant analysis</li>
</ul>
</section>
</div>
<div class="4u">
<section class="box">
<h3><a href="https://github.com/JuliaStats/HypothesisTests.jl">HypothesisTests</a></h3>
<p><strong>Hypothesis tests</strong></p>
<ul class="funlist">
<li>Parametric tests: t-tests</li>
<li>Nonparametric tests: binomial tests, sign tests, exact tests, U tests, rank tests, etc</li>
</ul>
</section>
</div>
</div>
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<h3><a href="https://github.com/JuliaStats/MLBase.jl">MLBase</a></h3>
<p><strong>Swiss knife for machine learning</strong></p>
<ul class="funlist">
<li>Data preprocessing</li>
<li>Score-based classification</li>
<li>Performance evaluation</li>
<li>Model selection, cross validation</li>
</ul>
</section>
</div>
<div class="4u">
<section class="box">
<h3><a href="https://github.com/JuliaStats/Distances.jl">Distances</a></h3>
<p><strong>Various distances between vectors</strong></p>
<ul class="funlist">
<li>A large variety of metrics</li>
<li>Efficient column-wise and pairwise computation</li>
<li>Support weighted distances</li>
</ul>
</section>
</div>
<div class="4u">
<section class="box">
<h3><a href="https://github.com/JuliaStats/KernelDensity.jl">KernelDensity</a></h3>
<p><strong>Kernel density estimation</strong></p>
<ul class="funlist">
<li>Kernel density estimation for univariate and bivariate data</li>
<li>User customization of interpolation points, kernel, and bandwidth</li>
</ul>
</section>
</div>
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<section class="box">
<h3><a href="https://github.com/JuliaStats/Clustering.jl">Clustering</a></h3>
<p><strong>Algorithms for data clustering</strong></p>
<ul class="funlist">
<li>K-means</li>
<li>K-medoids</li>
<li>Affinity propagation</li>
<li>Evaluation of clustering performance</li>
</ul>
</section>
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<div class="4u">
<section class="box">
<h3><a href="https://github.com/JuliaStats/GLM.jl">GLM</a></h3>
<p><strong>Generalized linear models</strong></p>
<ul class="funlist">
<li>Friendly API for fitting GLM to data</li>
<li>Work with data frames and formulas</li>
<li>A variety of link types</li>
<li>Optimized implementation</li>
</ul>
</section>
</div>
<div class="4u">
<section class="box">
<h3><a href="https://github.com/JuliaStats/NMF.jl">NMF</a></h3>
<p><strong>Nonnegative matrix factorization</strong></p>
<ul class="funlist">
<li>A variety of NMF algorithms, including Lee & Seung's, Projected ALS and projected gradient, with optimized implementation.</li>
<li>NNDSVD initialization</li>
</ul>
</section>
</div>
</div>
<div class="row">
<div class="4u">
<section class="box">
<h3><a href="https://github.com/JuliaStats/Lasso.jl">RegERMs</a></h3>
<p><strong>Lasso/Elastic Net linear and generalized linear models</strong></p>
<ul class="funlist">
<li>glmnet coordinate descent algorithm</li>
<li>Polynomial trend filtering</li>
<li>O(n) fused Lasso</li>
<li>Gamma Lasso (a concave regularization path glmnet variant)</li>
</ul>
</section>
</div>
<div class="4u">
<section class="box">
<h3><a href="https://github.com/JuliaStats/TimeSeries.jl">TimeSeries</a></h3>
<p><strong>Time series analysis</strong></p>
<ul class="funlist">
<li>Tools to represent, manipulate, and apply computation to time series data</li>
</ul>
</section>
</div>
</div>
</article>
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<h2>Community</h2>
<span>We have an active and friendly community.</span>
</header>
<p><strong>Forum</strong>: <a href="https://discourse.julialang.org/c/domain/stats">Statistics topic on the Julia Discourse</a></p>
<p><strong>Github page</strong>: <a href="https://github.com/JuliaStats">https://github.com/JuliaStats</a></p>
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