<?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>egrodri.r-universe.dev</title><link>https://egrodri.r-universe.dev</link><description>Recent package updates in egrodri</description><generator>R-universe</generator><image><url>https://github.com/egrodri.png</url><title>R packages by egrodri</title><link>https://egrodri.r-universe.dev</link></image><lastBuildDate>Mon, 29 Aug 2022 08:30:02 GMT</lastBuildDate><item><title>[egrodri] codacore 0.0.4</title><author>eg2912@columbia.edu (Elliott Gordon-Rodriguez)</author><description>In the context of high-throughput genetic data, CoDaCoRe
identifies a set of sparse biomarkers that are predictive of a
response variable of interest (Gordon-Rodriguez et al., 2021)
&lt;doi:10.1093/bioinformatics/btab645&gt;. More generally, CoDaCoRe
can be applied to any regression problem where the independent
variable is Compositional (CoDa), to derive a set of
scale-invariant log-ratios (ILR or SLR) that are maximally
associated to a dependent variable.</description><link>https://github.com/r-universe/egrodri/actions/runs/35324926138</link><pubDate>Mon, 29 Aug 2022 08:30:02 GMT</pubDate><r:package>codacore</r:package><r:version>0.0.4</r:version><r:status>success</r:status><r:repository>https://egrodri.r-universe.dev</r:repository><r:upstream>https://github.com/cran/codacore</r:upstream><r:article><r:source>guide.Rmd</r:source><r:filename>guide.html</r:filename><r:title>CoDaCoRe guide</r:title><r:created>2022-01-07 09:10:02</r:created><r:modified>2022-08-29 08:30:02</r:modified></r:article></item></channel></rss>