<?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>luluo1999.r-universe.dev</title><link>https://luluo1999.r-universe.dev</link><description>Recent package updates in luluo1999</description><generator>R-universe</generator><image><url>https://github.com/luluo1999.png</url><title>R packages by luluo1999</title><link>https://luluo1999.r-universe.dev</link></image><lastBuildDate>Sat, 25 Oct 2025 12:54:48 GMT</lastBuildDate><item><title>[luluo1999] LRQVB 1.0.0</title><author>luolu@stu.ynu.edu.cn (Lu Luo)</author><description>A Low Rank Correction Variational Bayesian algorithm for
high-dimensional multi-source heterogeneous quantile linear
models. More details have been written up in a paper submitted
to the journal Statistics in Medicine, and the details of
variational Bayesian methods can be found in Ray and Szabo
(2021) &lt;doi:10.1080/01621459.2020.1847121&gt;. It simultaneously
performs parameter estimation and variable selection. The
algorithm supports two model settings: (1) local models, where
variable selection is only applied to homogeneous coefficients,
and (2) global models, where variable selection is also
performed on heterogeneous coefficients. Two forms of parameter
estimation are output: one is the standard variational Bayesian
estimation, and the other is the variational Bayesian
estimation corrected with low-rank adjustment.</description><link>https://github.com/r-universe/luluo1999/actions/runs/26219851147</link><pubDate>Sat, 25 Oct 2025 12:54:48 GMT</pubDate><r:package>LRQVB</r:package><r:version>1.0.0</r:version><r:status>success</r:status><r:repository>https://luluo1999.r-universe.dev</r:repository><r:upstream>https://github.com/cran/LRQVB</r:upstream></item><item><title>[luluo1999] VBMS 1.0.0</title><author>luolu@stu.ynu.edu.cn (Lu Luo)</author><description>A Variational Bayesian algorithm for high-dimensional
multi-source heterogeneous linear models. More details have
been written up in a paper submitted to the journal Statistics
in Medicine, and the details of variational Bayesian methods
can be found in Ray and Szabo (2021)
&lt;doi:10.1080/01621459.2020.1847121&gt;. It simultaneously performs
parameter estimation and variable selection. The algorithm
supports two model settings: (1) local models, where variable
selection is only applied to homogeneous coefficients, and (2)
global models, where variable selection is also performed on
heterogeneous coefficients. Two forms of Spike-and-Slab priors
are available: the Laplace distribution and the Gaussian
distribution as the Slab component.</description><link>https://github.com/r-universe/luluo1999/actions/runs/27057624232</link><pubDate>Wed, 08 Oct 2025 19:40:13 GMT</pubDate><r:package>VBMS</r:package><r:version>1.0.0</r:version><r:status>success</r:status><r:repository>https://luluo1999.r-universe.dev</r:repository><r:upstream>https://github.com/cran/VBMS</r:upstream></item></channel></rss>