<?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>ksatohds.r-universe.dev</title><link>https://ksatohds.r-universe.dev</link><description>Recent package updates in ksatohds</description><generator>R-universe</generator><image><url>https://github.com/ksatohds.png</url><title>R packages by ksatohds</title><link>https://ksatohds.r-universe.dev</link></image><lastBuildDate>Sun, 14 Jun 2026 11:43:40 GMT</lastBuildDate><item><title>[ksatohds] nmfkc 0.8.2</title><author>kenichi-satoh@biwako.shiga-u.ac.jp (Kenichi Satoh)</author><description>Performs Non-negative Matrix Factorization (NMF) with
Kernel Covariates. Given an observation matrix and kernel
covariates, it optimizes both a basis matrix and a parameter
matrix. Notably, if the kernel matrix is an identity matrix,
the method simplifies to standard NMF. Also provides NMF with
Random Effects (NMF-RE) via nmfre(), which estimates a
mixed-effects model combining covariate-driven scores with
unit-specific random effects together with wild bootstrap
inference, and NMF-based Structural Equation Modeling (NMF-SEM)
via nmf.sem(), which fits a two-block input-output model for
blind source separation and path analysis. References: Satoh
(2025) &lt;doi:10.48550/arXiv.2403.05359&gt;; Satoh (2025)
&lt;doi:10.48550/arXiv.2510.10375&gt;; Satoh (2025)
&lt;doi:10.48550/arXiv.2512.18250&gt;; Satoh (2026)
&lt;doi:10.48550/arXiv.2603.01468&gt;; Satoh (2026)
&lt;doi:10.1007/s42081-025-00314-0&gt;.</description><link>https://github.com/r-universe/ksatohds/actions/runs/27497986777</link><pubDate>Sun, 14 Jun 2026 11:43:40 GMT</pubDate><r:package>nmfkc</r:package><r:version>0.8.2</r:version><r:status>success</r:status><r:repository>https://ksatohds.r-universe.dev</r:repository><r:upstream>https://github.com/ksatohds/nmfkc</r:upstream><r:article><r:source>rank-selection-with-nmfkc.Rmd</r:source><r:filename>rank-selection-with-nmfkc.html</r:filename><r:title>Choosing the NMF rank on data with a known true rank</r:title><r:created>2026-06-14 09:20:18</r:created><r:modified>2026-06-14 09:20:18</r:modified></r:article><r:article><r:source>classification-with-nmfkc.Rmd</r:source><r:filename>classification-with-nmfkc.html</r:filename><r:title>Classification with NMF-LAB</r:title><r:created>2025-10-13 04:49:23</r:created><r:modified>2026-03-29 11:11:57</r:modified></r:article><r:article><r:source>introduction-to-nmfkc.Rmd</r:source><r:filename>introduction-to-nmfkc.html</r:filename><r:title>Introduction to nmfkc</r:title><r:created>2025-10-13 04:49:23</r:created><r:modified>2025-11-24 21:22:05</r:modified></r:article><r:article><r:source>nmf-sem-with-nmfkc.Rmd</r:source><r:filename>nmf-sem-with-nmfkc.html</r:filename><r:title>NMF-FFB with nmfkc</r:title><r:created>2025-12-20 04:45:34</r:created><r:modified>2026-05-03 00:14:37</r:modified></r:article><r:article><r:source>nmf-re-with-nmfkc.Rmd</r:source><r:filename>nmf-re-with-nmfkc.html</r:filename><r:title>NMF-RE: Mixed-Effects Modeling with nmfkc</r:title><r:created>2026-03-02 20:53:29</r:created><r:modified>2026-03-29 07:13:22</r:modified></r:article><r:article><r:source>network-community-with-nmfkc.Rmd</r:source><r:filename>network-community-with-nmfkc.html</r:filename><r:title>Soft community detection in networks with nmfkc.net</r:title><r:created>2026-06-14 09:20:18</r:created><r:modified>2026-06-14 09:20:18</r:modified></r:article><r:article><r:source>timeseries-with-nmfkc.Rmd</r:source><r:filename>timeseries-with-nmfkc.html</r:filename><r:title>Time Series Analysis with NMF-VAR</r:title><r:created>2025-10-13 04:49:23</r:created><r:modified>2026-03-29 11:11:57</r:modified></r:article><r:article><r:source>topic-modeling-with-nmfkc.Rmd</r:source><r:filename>topic-modeling-with-nmfkc.html</r:filename><r:title>Topic Modeling with nmfkc</r:title><r:created>2025-10-13 04:49:23</r:created><r:modified>2026-06-14 09:20:18</r:modified></r:article></item></channel></rss>