<?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>anjazgodic.r-universe.dev</title><link>https://anjazgodic.r-universe.dev</link><description>Recent package updates in anjazgodic</description><generator>R-universe</generator><image><url>https://github.com/anjazgodic.png</url><title>R packages by anjazgodic</title><link>https://anjazgodic.r-universe.dev</link></image><lastBuildDate>Tue, 10 Mar 2026 02:27:24 GMT</lastBuildDate><item><title>[anjazgodic] lmmprobe 0.1.0</title><author>anja.zgodic@gmail.com (Anja Zgodic)</author><description>Implements a partitioned Empirical Bayes Expectation
Conditional Maximization (ECM) algorithm for sparse
high-dimensional linear mixed modeling as described in Zgodic,
Bai, Zhang, and McLain (2025) &lt;doi:10.1007/s11222-025-10649-z&gt;.
The package provides efficient estimation and inference for
mixed models with high-dimensional fixed effects.</description><link>https://github.com/r-universe/anjazgodic/actions/runs/27337946184</link><pubDate>Tue, 10 Mar 2026 02:27:24 GMT</pubDate><r:package>lmmprobe</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://anjazgodic.r-universe.dev</r:repository><r:upstream>https://github.com/anjazgodic/lmmprobe</r:upstream><r:article><r:source>lmmprobe-intro.Rmd</r:source><r:filename>lmmprobe-intro.html</r:filename><r:title>Introduction to lmmprobe</r:title><r:created>2026-02-26 23:18:55</r:created><r:modified>2026-03-10 02:27:24</r:modified></r:article></item></channel></rss>