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(This article was first published on <strong><a href="http://www.win-vector.com/blog/2016/11/vtreat-data-cleaning-and-preparation-article-now-available-on-arxiv/"> R – Win-Vector Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><a target="_blank" rel="nofollow" href="http://www.win-vector.com/site/staff/nina-zumel/">Nina Zumel</a> and <a target="_blank" rel="nofollow" href="http://www.win-vector.com/site/staff/john-mount/">I</a> are happy to announce a formal article discussing data preparation and cleaning using the <a target="_blank" rel="nofollow" href="https://cran.r-project.org/package=vtreat">vtreat</a> methodology is now available from <a target="_blank" rel="nofollow" href="https://arxiv.org/">arXiv.org</a> as citation <a target="_blank" rel="nofollow" href="https://arxiv.org/abs/1611.09477">arXiv:1611.09477 [stat.AP]</a>.</p>
<p><code>vtreat</code> is an <a target="_blank" rel="nofollow" href="https://cran.r-project.org/p">R</a> <code>data.frame</code> processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner. It prepares variables so that data has fewer exceptional cases, making it easier to safely use models in production. Common problems <code>vtreat</code> defends against include: <code>infinity</code>, <code>NA</code>, too many categorical levels, rare categorical levels, and new categorical levels (levels seen during application, but not during training). <code>vtreat::prepare</code> should be your first choice for real world data preparation and cleaning.</p>
<p>We hope this article will make getting started with <code>vtreat</code> much easier. We also hope this helps with citing the use of <code>vtreat</code> in scientific publications.<span></span></p>
<p>
We have also submitted a formal draft to <a target="_blank" rel="nofollow" href="https://www.jstatsoft.org/index">The Journal of Statistical Software</a>. JSS is a bit of a new venue for us, so we would appreciate any help we can get with the review process. 
<p>You can cite <a target="_blank" rel="nofollow" href="https://arxiv.org/abs/1611.09477">the current article</a> as:</p>
<p><code>
<pre>
@misc{vtreatarticle,
    title = {vtreat: a data.frame Processor for Predictive Modeling},
    author = {Nina Zumel and John Mount},
    year = {2016},
    month = {November},
    journal = {arXiv},
    date        = {2016-11-29},
    howpublished = {arXiv:1611.09477 [stat.AP] \url{https://arxiv.org/abs/1611.09477}},
    url = {https://arxiv.org/abs/1611.09477},
    urldate     = {2016-11-29},
    eprinttype  = {arxiv},
    pages = {1--40},
    eprint      = {arXiv:1611.09477 [stat.AP]}
  }
</pre>
<p>Zumel, N. and Mount, J. (2016). vtreat: a data.frame processor for predictive modeling. arXiv:1611.09477 [stat.AP] <a target="_blank" rel="nofollow" href="https://arxiv.org/abs/1611.09477">https://arxiv.org/abs/1611.09477</a>.<br>
</p>
<p>And you can cite the <a target="_blank" rel="nofollow" href="https://cran.r-project.org/package=vtreat"><code>vtreat</code> package</a> as:</p>
<p><code>
<pre>
@misc{vtreatpackage,
    title = {vtreat: A Statistically Sound data.frame Processor/Conditioner},
    author = {John Mount and Nina Zumel},
    year = {2016},
    note = {R package version 0.5.28},
    howpublished = {\url{https://CRAN.R-project.org/package=vtreat}},
    url = {https://CRAN.R-project.org/package=vtreat}
  }
</pre>
<p>Mount, J. and Zumel, N. (2016). vtreat: A statistically sound data.frame processor/conditioner. <a target="_blank" rel="nofollow" href="https://cran.r-project.org/package=vtreat">https://CRAN.R-project.org/package=vtreat</a>. R package version 0.5.28.<br>
</p>
<p>For more articles on <code>vtreat</code> please try <a target="_blank" rel="nofollow" href="http://www.win-vector.com/blog/tag/variable-treatment/">here</a> or <a target="_blank" rel="nofollow" href="http://www.win-vector.com/blog/tag/vtreat/">here</a>.</p>

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