<?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>senuyasara.r-universe.dev</title><link>https://senuyasara.r-universe.dev</link><description>Recent package updates in senuyasara</description><generator>R-universe</generator><image><url>https://github.com/senuyasara.png</url><title>R packages by senuyasara</title><link>https://senuyasara.r-universe.dev</link></image><lastBuildDate>Wed, 22 Oct 2025 00:33:00 GMT</lastBuildDate><item><title>[senuyasara] MOutliers 0.0.0.9000</title><author>first.last@example.com (Senuri Yasara)</author><description>Provides tools for detecting multivariate outliers in
numeric datasets using Mahalanobis distance, robust Minimum
Covariance Determinant (MCD), and Principal Component Analysis
(PCA)-based methods. The Mahalanobis distance calculations are
performed using an efficient C++ backend via Rcpp.</description><link>https://github.com/r-universe/senuyasara/actions/runs/27608872962</link><pubDate>Wed, 22 Oct 2025 00:33:00 GMT</pubDate><r:package>MOutliers</r:package><r:version>0.0.0.9000</r:version><r:status>success</r:status><r:repository>https://senuyasara.r-universe.dev</r:repository><r:upstream>https://github.com/senuyasara/multivariate_outlier_detection_r_package</r:upstream><r:article><r:source>multivariate_outliers.Rmd</r:source><r:filename>multivariate_outliers.html</r:filename><r:title>Multivariate Outlier Detection</r:title><r:created>2025-09-26 00:37:43</r:created><r:modified>2025-10-22 00:28:18</r:modified></r:article></item></channel></rss>