<?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>fannyyang73.r-universe.dev</title><link>https://fannyyang73.r-universe.dev</link><description>Recent package updates in fannyyang73</description><generator>R-universe</generator><image><url>https://github.com/fannyyang73.png</url><title>R packages by fannyyang73</title><link>https://fannyyang73.r-universe.dev</link></image><lastBuildDate>Sat, 11 Jul 2026 09:12:09 GMT</lastBuildDate><item><title>[fannyyang73] BSCB 1.0.0</title><author>fei.yang@manchester.ac.uk (Fei Yang)</author><description>Provides functions to construct two-sided Bayesian
simultaneous credible bands (BSCBs) for the regression curve in
univariate polynomial regression over a finite covariate
interval. Six methods are implemented, including Normal-Gamma
conjugate priors (with empirical Bayes, unit-information, and
g-prior hyperparameter specifications), non-conjugate priors
fitted via Hamiltonian Monte Carlo (HMC) using 'cmdstanr', and
a non-informative independent Jeffreys prior approach. Also
includes functions for computing the empirical simultaneous
coverage rate (ESCR) and posterior simultaneous coverage
probability (PSCP), enabling performance comparison across
methods. The methodology is described in: Yang, F., Han, Y.,
Liu, W., &amp; Hall, I. (2026). &quot;Bayesian simultaneous credible
bands for polynomial regression&quot;
&lt;doi:10.48550/arXiv.2606.28015&gt;.</description><link>https://github.com/r-universe/fannyyang73/actions/runs/29147358863</link><pubDate>Sat, 11 Jul 2026 09:12:09 GMT</pubDate><r:package>BSCB</r:package><r:version>1.0.0</r:version><r:status>success</r:status><r:repository>https://fannyyang73.r-universe.dev</r:repository><r:upstream>https://github.com/fannyyang73/bscb</r:upstream><r:article><r:source>BSCB-vignette.Rmd</r:source><r:filename>BSCB-vignette.html</r:filename><r:title>BSCB-vignette</r:title><r:created>2026-04-15 15:15:19</r:created><r:modified>2026-04-16 11:04:11</r:modified></r:article></item></channel></rss>