<?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>angelayustat.r-universe.dev</title><link>https://angelayustat.r-universe.dev</link><description>Recent package updates in angelayustat</description><generator>R-universe</generator><image><url>https://github.com/angelayustat.png</url><title>R packages by angelayustat</title><link>https://angelayustat.r-universe.dev</link></image><lastBuildDate>Thu, 13 Nov 2025 00:01:46 GMT</lastBuildDate><item><title>[angelayustat] SCoRES 0.1.2</title><author>angela.yu@emory.edu (Zhuoran Yu)</author><description>Provides computational tools for estimating inverse
regions and constructing the corresponding simultaneous outer
and inner confidence regions. Acceptable input includes both
one-dimensional and two-dimensional data for linear, logistic,
functional, and spatial generalized least squares regression
models. Functions are also available for constructing
simultaneous confidence bands (SCBs) for these models. The
definition of simultaneous confidence regions (SCRs) follows
Sommerfeld et al. (2018) &lt;doi:10.1080/01621459.2017.1341838&gt;.
Methods for estimating inverse regions, SCRs, and the
nonparametric bootstrap are based on Ren et al. (2024)
&lt;doi:10.1093/jrsssc/qlae027&gt;. Methods for constructing SCBs are
described in Crainiceanu et al. (2024)
&lt;doi:10.1201/9781003278726&gt; and Telschow et al. (2022)
&lt;doi:10.1016/j.jspi.2021.05.008&gt;.</description><link>https://github.com/r-universe/angelayustat/actions/runs/29479218575</link><pubDate>Thu, 13 Nov 2025 00:01:46 GMT</pubDate><r:package>SCoRES</r:package><r:version>0.1.2</r:version><r:status>success</r:status><r:repository>https://angelayustat.r-universe.dev</r:repository><r:upstream>https://github.com/angelayustat/scores</r:upstream><r:article><r:source>Functional_Data_Example.Rmd</r:source><r:filename>Functional_Data_Example.html</r:filename><r:title>Functional_Data_Example</r:title><r:created>2025-07-22 04:11:39</r:created><r:modified>2025-11-12 23:36:31</r:modified></r:article><r:article><r:source>Geographic_Data_Example.Rmd</r:source><r:filename>Geographic_Data_Example.html</r:filename><r:title>Geographic_Data_Example</r:title><r:created>2025-08-11 10:20:43</r:created><r:modified>2025-11-12 23:36:31</r:modified></r:article><r:article><r:source>Linear_Model_Example.Rmd</r:source><r:filename>Linear_Model_Example.html</r:filename><r:title>Linear_Model_Example</r:title><r:created>2025-07-22 04:11:39</r:created><r:modified>2025-11-12 23:36:31</r:modified></r:article><r:article><r:source>Methods.Rmd</r:source><r:filename>Methods.html</r:filename><r:title>Methods</r:title><r:created>2025-07-22 04:11:39</r:created><r:modified>2025-11-04 16:22:47</r:modified></r:article><r:article><r:source>SCoRES.Rmd</r:source><r:filename>SCoRES.html</r:filename><r:title>SCoRES: a vignette</r:title><r:created>2025-07-28 00:02:50</r:created><r:modified>2025-10-27 17:52:08</r:modified></r:article></item></channel></rss>