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(This article was first published on <strong><a href="http://www.win-vector.com/blog/2018/01/latest-vtreat-up-on-cran/"> R – Win-Vector Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>There is a new version of the <a rel="nofollow" href="https://www.r-project.org/" target="_blank">R</a> package <a rel="nofollow" href="https://winvector.github.io/vtreat/" target="_blank">vtreat</a> now up on <a rel="nofollow" href="https://cran.r-project.org/package=vtreat" target="_blank">CRAN</a>.</p>
<p>vtreat is an essential data preparation system for predictive modeling that helps defend your predictive modeling work against real world data issues including:</p>
<ul>
<li>High cardinality categorical variables</li>
<li>Rare levels (including new or novel levels during application) in categorical variables</li>
<li>Missing data (random or systematic)</li>
<li>Irrelevant variables/columns</li>
<li>Nested model bias, and other over-fit issues.</li>
</ul>
<p>vtreat also includes excellent, citable, documentation: <a rel="nofollow" href="https://arxiv.org/abs/1611.09477" target="_blank">vtreat: a data.frame Processor for Predictive Modeling</a>.</p>
<p>For this release I want to thank everybody who generously donated their time to submit an issue or build a git pull-request. In particular:</p>
<ul>
<li>Vadim Khotilovich, who found and fixed a major performance problem in the y-stratified sampling.</li>
<li>Lawrence Wu, who has been donating documentation fixes.</li>
<li>Peter Hurford, who has been donating documentation fixes.</li>
</ul>
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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/2018/01/latest-vtreat-up-on-cran/"> R – Win-Vector Blog</a></strong>.</div>
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