<?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>geauxsl.r-universe.dev</title><link>https://geauxsl.r-universe.dev</link><description>Recent package updates in geauxsl</description><generator>R-universe</generator><image><url>https://github.com/geauxsl.png</url><title>R packages by geauxsl</title><link>https://geauxsl.r-universe.dev</link></image><lastBuildDate>Fri, 10 Apr 2026 11:15:01 GMT</lastBuildDate><item><title>[geauxsl] TAG 0.7.1</title><author>lhlin@gsu.edu (Li-Hsiang Lin)</author><description>Implement the transformed additive Gaussian (TAG) process
and the transformed approximately additive Gaussian (TAAG)
process proposed in Lin and Joseph (2020)
&lt;DOI:10.1080/00401706.2019.1665592&gt;. These functions can be
used to model deterministic computer experiments, obtain
predictions at new inputs, and quantify the uncertainty of the
predictions. This research is supported by a U.S. National
Science Foundation grant DMS-1712642 and a U.S. Army Research
Office grant W911NF-17-1-0007.</description><link>https://github.com/r-universe/geauxsl/actions/runs/25790043225</link><pubDate>Fri, 10 Apr 2026 11:15:01 GMT</pubDate><r:package>TAG</r:package><r:version>0.7.1</r:version><r:status>success</r:status><r:repository>https://geauxsl.r-universe.dev</r:repository><r:upstream>https://github.com/cran/TAG</r:upstream></item></channel></rss>