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(This article was first published on <strong><a href="http://www.win-vector.com/blog/2017/09/upcoming-data-preparation-and-modeling-article-series/"> R – Win-Vector Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>I am pleased to announce that <a rel="nofollow" href="https://winvector.github.io/vtreat/" target="_blank"><code>vtreat</code></a> version 0.6.0 is now <a rel="nofollow" href="https://cran.r-project.org/package=vtreat" target="_blank">available to <code>R</code> users on CRAN</a>.</p>
<p><center><br>
<a rel="nofollow" href="https://winvector.github.io/vtreat/" target="_blank"><img border="0" src="https://i0.wp.com/www.win-vector.com/blog/wp-content/uploads/2017/09/vtreat.png?w=660" alt="Vtreat" title="vtreat.png" data-recalc-dims="1"></a><br>
</center></p>
<p><code>vtreat</code> is an <em>excellent</em> way to prepare data for machine learning, statistical inference, and predictive analytic projects. If you are an <a rel="nofollow" href="https://cran.r-project.org/" target="_blank"><code>R</code></a> user we <em>strongly</em> suggest you incorporate <code>vtreat</code> into your projects.<span></span></p>
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<p><code>vtreat</code> handles, in a statistically sound fashion:</p>
<ul>
<li>Missing values.</li>
<li>Encoding of categorical values for regularized inference and machine learning techniques.</li>
<li>Categorical variables with very many values.</li>
<li>Novel categorical values (that is values not seen during training).</li>
<li>Variable pruning.</li>
<li><a rel="nofollow" href="http://www.win-vector.com/blog/2016/06/y-aware-scaling-in-context/" target="_blank">y-aware scaling</a>.</li>
<li><a rel="nofollow" href="http://www.win-vector.com/blog/tag/structured-cross-validation/" target="_blank">Structured cross-validation</a>.</li>
<li><a rel="nofollow" href="http://www.win-vector.com/blog/2016/04/on-nested-models/" target="_blank">Mitigating nested model bias</a>.</li>
</ul>
<p>In our (biased) opinion opinion <code>vtreat</code> has <em>the best</em> methodology and <a rel="nofollow" href="https://arxiv.org/abs/1611.09477" target="_blank">documentation</a> for these important data cleaning and preparation steps. <code>vtreat</code>‘s current public open-source implementation is for in-memory <code>R</code> analysis (we are considering ports and certifying ports of the package some time in the future, possibly for: <code>data.table</code>, <code>Spark</code>, <code>Python</code>/<code>Pandas</code>, and <code>SQL</code>).</p>
<p><code>vtreat</code> brings <em>a lot</em> of power, sophistication, and convenience to your analyses, without a lot of trouble.</p>
<p>A new feature of <code>vtreat</code> version 0.6.0 is called “custom coders.” <a rel="nofollow" href="http://www.win-vector.com/" target="_blank">Win-Vector LLC</a>‘s <a rel="nofollow" href="http://www.win-vector.com/site/staff/nina-zumel/" target="_blank">Dr. Nina Zumel</a> is going to start a short article series to show how this new interface can be used to extend <code>vtreat</code> methodology to include the very powerful method of partial pooled inference (a term she will spend some time clearly defining and explaining). Time permitting, we may continue with articles on other applications of custom coding including: ordinal/faithful coders, monotone coders, unimodal coders, and set-valued coders.</p>
<p><em>Please</em> help us share and promote this article series, which should start in a couple of days. This should be a fun chance to share very powerful methods with your colleagues.</p>
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<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="http://www.win-vector.com/blog/2017/09/upcoming-data-preparation-and-modeling-article-series/"> R – Win-Vector Blog</a></strong>.</div>
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