<?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>nathanbresette.r-universe.dev</title><link>https://nathanbresette.r-universe.dev</link><description>Recent package updates in nathanbresette</description><generator>R-universe</generator><image><url>https://github.com/nathanbresette.png</url><title>R packages by nathanbresette</title><link>https://nathanbresette.r-universe.dev</link></image><lastBuildDate>Fri, 18 Sep 2026 19:02:00 GMT</lastBuildDate><item><title>[biocstaging] woven 0.99.3</title><author>nathanbresette04@gmail.com (Nathan Bresette)</author><description>Supervised multi-omics integration for block-missing
(&quot;ragged&quot;) data. WOVEN learns a shared latent space across V
omics modalities using only fully-observed anchor subjects,
then projects block-missing subjects via Nystrom extension
without feature-level imputation. Supervision enters through a
label-augmented cross-covariance (analogous to DIABLO), and
projection matrices can optionally be ridge-regularized and
univariately feature-screened for high-dimensional modalities.
Designed for comparative effectiveness research where
intersection-only methods introduce selection bias.</description><link>https://github.com/r-universe/biocstaging/actions/runs/35387809969</link><pubDate>Fri, 18 Sep 2026 19:02:00 GMT</pubDate><r:package>woven</r:package><r:version>0.99.3</r:version><r:status>success</r:status><r:repository>https://biocstaging.r-universe.dev</r:repository><r:upstream>https://github.com/BiocStaging/woven</r:upstream><r:article><r:source>woven-intro.Rmd</r:source><r:filename>woven-intro.html</r:filename><r:title>Introduction to WOVEN: Multi-Omics Integration for Incomplete Patient Data</r:title><r:created>2026-06-16 13:50:42</r:created><r:modified>2026-09-17 21:23:33</r:modified></r:article></item><item><title>[bioc] MeLSI 1.1.7</title><author>nathanbresette04@gmail.com (Nathan Bresette)</author><description>MeLSI (Metric Learning for Statistical Inference) is a
novel machine learning method for microbiome data analysis that
learns optimal distance metrics to improve statistical power in
detecting group differences. Unlike traditional distance
metrics (Bray-Curtis, Euclidean, Jaccard), MeLSI adapts to the
specific characteristics of your dataset to maximize separation
between groups. The method uses an ensemble of weak learners to
identify which microbial features drive group differences,
providing both improved statistical power and biological
interpretability through feature importance weights.</description><link>https://github.com/r-universe/bioc/actions/runs/38872670373</link><pubDate>Wed, 17 Jun 2026 21:23:49 GMT</pubDate><r:package>MeLSI</r:package><r:version>1.1.7</r:version><r:status>success</r:status><r:repository>https://bioc.r-universe.dev</r:repository><r:upstream>https://github.com/bioc/MeLSI</r:upstream><r:article><r:source>melsi_tutorial.Rmd</r:source><r:filename>melsi_tutorial.html</r:filename><r:title>MeLSI Tutorial: Basic Usage and Examples</r:title><r:created>2025-10-02 14:53:34</r:created><r:modified>2026-06-17 15:06:44</r:modified></r:article></item></channel></rss>