<?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>artisto123.r-universe.dev</title><link>https://artisto123.r-universe.dev</link><description>Recent package updates in artisto123</description><generator>R-universe</generator><image><url>https://github.com/artisto123.png</url><title>R packages by artisto123</title><link>https://artisto123.r-universe.dev</link></image><lastBuildDate>Tue, 30 Jun 2026 21:11:13 GMT</lastBuildDate><item><title>[artisto123] sportsfeatures 0.1.0</title><author>ma.abbas3107@gmail.com (Mohammad Abbas)</author><description>A synthetic, longitudinal athletic dataset generated
through a transparent, rule-based simulation engine. Captures
individual activity sessions across multiple athletes,
environmental conditions, and physiological responses.
Specifically designed as an alternative to legacy teaching
datasets by introducing realistic hierarchical repeated
measures, complex two-way covariate interactions, and a
deliberate Missing Not At Random (MNAR) tracking mechanism
suitable for advanced imputation workflows. Methodologies
implemented are based on van Buuren (2018)
&lt;doi:10.1201/9780429492259&gt; and Bates et al. (2015)
&lt;doi:10.18637/jss.v067.i01&gt;.</description><link>https://github.com/r-universe/artisto123/actions/runs/28505447521</link><pubDate>Tue, 30 Jun 2026 21:11:13 GMT</pubDate><r:package>sportsfeatures</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://artisto123.r-universe.dev</r:repository><r:upstream>https://github.com/cran/sportsfeatures</r:upstream></item></channel></rss>