<?xml version="1.0" encoding="utf-8" ?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:r="https://r-universe.dev"><channel><title>phillip-sherlock.r-universe.dev</title><link>https://phillip-sherlock.r-universe.dev</link><description>Recent package updates in phillip-sherlock</description><generator>R-universe</generator><image><url>https://github.com/phillip-sherlock.png</url><title>R packages by phillip-sherlock</title><link>https://phillip-sherlock.r-universe.dev</link></image><lastBuildDate>Sun, 31 May 2026 20:36:53 GMT</lastBuildDate><item><title>[phillip-sherlock] ctreeMI 0.1.0</title><author>phillip.sherlock@ufl.edu (Phillip Sherlock)</author><description>Implements the stacked-imputation workflow for conditional
inference trees ('ctree') described in Sherlock et al. (2026)
&lt;doi:10.1080/00273171.2026.2661244&gt;. When data contain missing
values, multiply imputed datasets (e.g., from 'mice') are
stacked vertically and a single 'ctree' is fit on the combined
data. To correct for the artificially inflated sample size
introduced by stacking, the pruning significance threshold is
divided by the number of imputations M (the Stack/M
correction), producing a conservative but interpretable single
tree that incorporates imputation uncertainty without requiring
pooling of structurally different trees. Also exports
stack_imputations() and rescale_alpha() as standalone
utilities. The underlying 'ctree' algorithm is provided by
'partykit' (Hothorn &amp; Zeileis, 2015; Hothorn, Hornik &amp; Zeileis,
2006 &lt;doi:10.1198/106186006X133933&gt;).</description><link>https://github.com/r-universe/phillip-sherlock/actions/runs/29145488652</link><pubDate>Sun, 31 May 2026 20:36:53 GMT</pubDate><r:package>ctreeMI</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://phillip-sherlock.r-universe.dev</r:repository><r:upstream>https://github.com/phillip-sherlock/ctreemi</r:upstream></item></channel></rss>