<?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>merolagio.r-universe.dev</title><link>https://merolagio.r-universe.dev</link><description>Recent package updates in merolagio</description><generator>R-universe</generator><image><url>https://github.com/merolagio.png</url><title>R packages by merolagio</title><link>https://merolagio.r-universe.dev</link></image><lastBuildDate>Mon, 06 Jul 2026 00:16:19 GMT</lastBuildDate><item><title>[merolagio] spca 1.1.1</title><author>merolagio@gmail.com (Giovanni Maria Merola)</author><description>Implements least-squares sparse principal component
analysis (LS-SPCA). The approach follows Merola (2015)
&lt;doi:10.1111/anzs.12128&gt; and Merola and Chen (2019)
&lt;doi:10.1016/j.jmva.2019.04.001&gt;.</description><link>https://github.com/r-universe/merolagio/actions/runs/29145481543</link><pubDate>Mon, 06 Jul 2026 00:16:19 GMT</pubDate><r:package>spca</r:package><r:version>1.1.1</r:version><r:status>success</r:status><r:repository>https://merolagio.r-universe.dev</r:repository><r:upstream>https://github.com/merolagio/spca</r:upstream><r:article><r:source>spca_extended_vignette.Rmd</r:source><r:filename>spca_extended_vignette.html</r:filename><r:title>Computing Least Squares Sparse Principal Components with spca</r:title><r:created>2026-06-22 04:05:12</r:created><r:modified>2026-07-03 01:57:36</r:modified></r:article><r:article><r:source>spca_intro.Rmd</r:source><r:filename>spca_intro.html</r:filename><r:title>Introduction to the spca package</r:title><r:created>2026-05-30 23:43:26</r:created><r:modified>2026-07-06 00:01:24</r:modified></r:article></item></channel></rss>