<?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>davdittrich.r-universe.dev</title><link>https://davdittrich.r-universe.dev</link><description>Recent package updates in davdittrich</description><generator>R-universe</generator><image><url>https://github.com/davdittrich.png</url><title>R packages by davdittrich</title><link>https://davdittrich.r-universe.dev</link></image><lastBuildDate>Mon, 04 May 2026 16:08:00 GMT</lastBuildDate><item><title>[davdittrich] robscale 0.5.4</title><author>davd@economicscience.net (Dennis Alexis Valin Dittrich)</author><description>Estimates robust location and scale parameters using
platform-specific Single Instruction, Multiple Data (SIMD)
vectorization and Intel Threading Building Blocks (TBB) for
parallel processing. Implements a novel variance-weighted
ensemble estimator that adaptively combines all available
statistics. Methods include logistic M-estimators, the
estimators of Rousseeuw and Croux (1993), the Gini mean
difference, the scaled Median Absolute Deviation (MAD), the
scaled Interquartile Range (IQR), and unbiased standard
deviations. Achieves substantial speedups over existing
implementations through an 'Rcpp' backend with fused
single-buffer algorithms that halve memory traffic for MAD and
M-scale estimation, and a unified dispatcher that automatically
selects the optimal estimator based on sample size.</description><link>https://github.com/r-universe/davdittrich/actions/runs/26657701523</link><pubDate>Mon, 04 May 2026 16:08:00 GMT</pubDate><r:package>robscale</r:package><r:version>0.5.4</r:version><r:status>success</r:status><r:repository>https://davdittrich.r-universe.dev</r:repository><r:upstream>https://github.com/davdittrich/robscale</r:upstream><r:article><r:source>robscale-intro.Rmd</r:source><r:filename>robscale-intro.html</r:filename><r:title>Introduction to robscale</r:title><r:created>2026-03-25 20:42:06</r:created><r:modified>2026-03-30 00:08:53</r:modified></r:article></item></channel></rss>