<?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>ultanpdoherty.r-universe.dev</title><link>https://ultanpdoherty.r-universe.dev</link><description>Recent package updates in ultanpdoherty</description><generator>R-universe</generator><image><url>https://github.com/ultanpdoherty.png</url><title>R packages by ultanpdoherty</title><link>https://ultanpdoherty.r-universe.dev</link></image><lastBuildDate>Sat, 31 Oct 2026 22:07:39 GMT</lastBuildDate><item><title>[ultanpdoherty] outlierMBC 0.0.1.0003</title><author>dohertyu@tcd.ie (Ultán P. Doherty)</author><description>Sequential outlier identification for Gaussian mixture
models using the distribution of Mahalanobis distances. The
optimal number of outliers is chosen based on the dissimilarity
between the theoretical and observed distributions of the
scaled squared sample Mahalanobis distances. Also includes an
extension for Gaussian linear cluster-weighted models using the
distribution of studentized residuals. Doherty, McNicholas, and
White (2025) &lt;doi:10.48550/arXiv.2505.11668&gt;.</description><link>https://github.com/r-universe/ultanpdoherty/actions/runs/41249874864</link><pubDate>Sat, 31 Oct 2026 22:07:39 GMT</pubDate><r:package>outlierMBC</r:package><r:version>0.0.1.0003</r:version><r:status>success</r:status><r:repository>https://ultanpdoherty.r-universe.dev</r:repository><r:upstream>https://github.com/ultanpdoherty/outliermbc</r:upstream></item></channel></rss>