<?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>authorssubmission.r-universe.dev</title><link>https://authorssubmission.r-universe.dev</link><description>Recent package updates in authorssubmission</description><generator>R-universe</generator><image><url>https://github.com/authorssubmission.png</url><title>R packages by authorssubmission</title><link>https://authorssubmission.r-universe.dev</link></image><lastBuildDate>Thu, 23 Oct 2025 15:04:27 GMT</lastBuildDate><item><title>[giorgiazaccaria] PUGMM 0.1.2</title><author>giorgia.zaccaria@unimib.it (Giorgia Zaccaria)</author><description>Parsimonious Ultrametric Gaussian Mixture Models via
grouped coordinate ascent (equivalent to EM) algorithm
characterized by the inspection of hierarchical relationships
among variables via parsimonious extended ultrametric
covariance structures. The methodologies are described in
Cavicchia, Vichi, Zaccaria (2024)
&lt;doi:10.1007/s11222-024-10405-9&gt;, (2022)
&lt;doi:10.1007/s11634-021-00488-x&gt; and (2020)
&lt;doi:10.1007/s11634-020-00400-z&gt;.</description><link>https://github.com/r-universe/giorgiazaccaria/actions/runs/37603542578</link><pubDate>Thu, 23 Oct 2025 15:04:27 GMT</pubDate><r:package>PUGMM</r:package><r:version>0.1.2</r:version><r:status>success</r:status><r:repository>https://giorgiazaccaria.r-universe.dev</r:repository><r:upstream>https://github.com/giorgiazaccaria/pugmm</r:upstream></item></channel></rss>