<?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>gabrielarpino.r-universe.dev</title><link>https://gabrielarpino.r-universe.dev</link><description>Recent package updates in gabrielarpino</description><generator>R-universe</generator><image><url>https://github.com/gabrielarpino.png</url><title>R packages by gabrielarpino</title><link>https://gabrielarpino.r-universe.dev</link></image><lastBuildDate>Tue, 21 Apr 2026 02:04:15 GMT</lastBuildDate><item><title>[gabrielarpino] weightederm 0.1.0</title><author>arpino.gabriel@gmail.com (Gabriel Arpino)</author><description>R interface to the 'weightederm' package for 'Python',
which provides 'scikit-learn'-style estimators for offline
change point regression (data segmentation) via weighted
empirical risk minimization. Supports least-squares, Huber, and
logistic losses with fixed or cross-validated numbers of change
points. Wraps 'Python' via 'reticulate'. Arpino and
Venkataramanan (2026) &lt;doi:10.48550/arXiv.2604.11746&gt;.</description><link>https://github.com/r-universe/gabrielarpino/actions/runs/26582099272</link><pubDate>Tue, 21 Apr 2026 02:04:15 GMT</pubDate><r:package>weightederm</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://gabrielarpino.r-universe.dev</r:repository><r:upstream>https://github.com/gabrielarpino/weightederm-r</r:upstream></item></channel></rss>