<?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>bosangir.r-universe.dev</title><link>https://bosangir.r-universe.dev</link><description>Recent package updates in bosangir</description><generator>R-universe</generator><image><url>https://github.com/bosangir.png</url><title>R packages by bosangir</title><link>https://bosangir.r-universe.dev</link></image><lastBuildDate>Wed, 28 Oct 2026 10:40:14 GMT</lastBuildDate><item><title>[lucp12891] MosaiClusteR 0.1.4</title><author>b.osangir@gmail.com (Bernard Isekah Osangir)</author><description>An umbrella framework (&quot;MoSaIC:&quot; Multi-Omics Similarity
Aggregation and Integrative Clustering in R) that unifies a
large collection of multi-source / multi-omics clustering
methodologies behind a single, consistent list-of-matrices
interface. It spans five integration paradigms - direct,
similarity-based, graph-based, voting-based consensus, and
hierarchy-based - and bundles a complete downstream workflow
for method comparison and evaluation. It supports comparing
many algorithms on the same footing, a data-nugget based
feature-weighting scheme as an alternative to variance
weighting, and a downstream suite for cluster characterisation,
visualisation and biological interpretation.</description><link>https://github.com/r-universe/lucp12891/actions/runs/40702780523</link><pubDate>Wed, 28 Oct 2026 10:40:14 GMT</pubDate><r:package>MosaiClusteR</r:package><r:version>0.1.4</r:version><r:status>success</r:status><r:repository>https://lucp12891.r-universe.dev</r:repository><r:upstream>https://github.com/lucp12891/mosaicluster</r:upstream><r:article><r:source>MosaiClusteR.Rmd</r:source><r:filename>MosaiClusteR.html</r:filename><r:title>MosaiClusteR: an umbrella framework for multi-source and multi-omics clustering</r:title><r:created>2026-07-03 19:19:06</r:created><r:modified>2026-10-28 10:29:59</r:modified></r:article></item><item><title>[neu-data] saydo 0.1.0</title><author>b.osangir@gmail.com (Bernard Isekah Osangir)</author><description>Measures the say-do gap in mixed-methods data: whether
themes in what people say go with differences in what they do.
Codes free text against a codebook with large language models
through 'ellmer', or any coder function, and validates the
codes against a human-coded sample with Cohen's kappa,
precision and recall. Links the codes to participants'
behavioural or outcome data, aggregating segment-level
transcripts to participants where needed, and tests each theme
against each metric with Welch's t-test and multiple-comparison
adjustment. Developed by Neudata Consulting Ltd.</description><link>https://github.com/r-universe/neu-data/actions/runs/40868961962</link><pubDate>Sun, 18 Oct 2026 12:53:04 GMT</pubDate><r:package>saydo</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://neu-data.r-universe.dev</r:repository><r:upstream>https://github.com/neu-data/saydo</r:upstream><r:article><r:source>saydo.Rmd</r:source><r:filename>saydo.html</r:filename><r:title>Getting started with saydo</r:title><r:created>2026-10-18 12:26:18</r:created><r:modified>2026-10-18 12:26:18</r:modified></r:article></item></channel></rss>