<?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>szumx123.r-universe.dev</title><link>https://szumx123.r-universe.dev</link><description>Recent package updates in szumx123</description><generator>R-universe</generator><image><url>https://github.com/szumx123.png</url><title>R packages by szumx123</title><link>https://szumx123.r-universe.dev</link></image><lastBuildDate>Mon, 02 Mar 2026 14:40:02 GMT</lastBuildDate><item><title>[szumx123] dpGMM 1.0.0</title><author>kamila.szumala@polsl.pl (Kamila Szumala)</author><description>Gaussian mixture modeling of one- and two-dimensional
data, provided in original or binned form, with an option to
estimate the number of model components. The method uses
Gaussian Mixture Models (GMM) with initial parameters
determined by a dynamic programming algorithm, leading to
stable and reproducible model fitting. For more details see
Zyla, J., Szumala, K., Polanski, A., Polanska, J., &amp; Marczyk,
M. (2026) &lt;doi:10.1016/j.jocs.2026.102811&gt;.</description><link>https://github.com/r-universe/szumx123/actions/runs/39405012790</link><pubDate>Mon, 02 Mar 2026 14:40:02 GMT</pubDate><r:package>dpGMM</r:package><r:version>1.0.0</r:version><r:status>success</r:status><r:repository>https://szumx123.r-universe.dev</r:repository><r:upstream>https://github.com/cran/dpGMM</r:upstream></item></channel></rss>