<?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>youyifong.r-universe.dev</title><link>https://youyifong.r-universe.dev</link><description>Recent package updates in youyifong</description><generator>R-universe</generator><image><url>https://github.com/youyifong.png</url><title>R packages by youyifong</title><link>https://youyifong.r-universe.dev</link></image><lastBuildDate>Tue, 24 Mar 2026 20:39:47 GMT</lastBuildDate><item><title>[youyifong] kyotil 2025.12-12</title><author>youyifong@gmail.com (Youyi Fong)</author><description>Helper functions for creating formatted summary of
regression models, writing publication-ready tables to latex
files, and running Monte Carlo experiments.</description><link>https://github.com/r-universe/youyifong/actions/runs/23515248031</link><pubDate>Tue, 24 Mar 2026 20:39:47 GMT</pubDate><r:package>kyotil</r:package><r:version>2025.12-12</r:version><r:status>success</r:status><r:repository>https://youyifong.r-universe.dev</r:repository><r:upstream>https://github.com/youyifong/kyotil</r:upstream><r:article><r:source>kyotil-vignette.pdf.asis</r:source><r:filename>kyotil-vignette.pdf</r:filename><r:title>Fitting Threshold Regression Models Using chngpt</r:title><r:created>2021-02-04 19:39:04</r:created><r:modified>2021-02-04 19:39:04</r:modified></r:article></item><item><title>[youyifong] marginalizedRisk 2026.2-4</title><author>youyifong@gmail.com (Youyi Fong)</author><description>Estimates risk as a function of a marker by integrating
over other covariates in a conditional risk model.</description><link>https://github.com/r-universe/youyifong/actions/runs/23998322358</link><pubDate>Wed, 04 Feb 2026 20:17:23 GMT</pubDate><r:package>marginalizedRisk</r:package><r:version>2026.2-4</r:version><r:status>success</r:status><r:repository>https://youyifong.r-universe.dev</r:repository><r:upstream>https://github.com/youyifong/marginalizedrisk</r:upstream></item><item><title>[youyifong] mdw 2024.8-1</title><author>youyifong@gmail.com (Youyi Fong)</author><description>Dimension-reduction methods aim at defining a score that
maximizes signal diversity. Three approaches, tree weight,
maximum entropy weights, and maximum variance weights are
provided. These methods are described in He and Fong (2019)
&lt;DOI:10.1002/sim.8212&gt;.</description><link>https://github.com/r-universe/youyifong/actions/runs/24277373447</link><pubDate>Sun, 27 Jul 2025 18:28:32 GMT</pubDate><r:package>mdw</r:package><r:version>2024.8-1</r:version><r:status>success</r:status><r:repository>https://youyifong.r-universe.dev</r:repository><r:upstream>https://github.com/youyifong/mdw</r:upstream><r:article><r:source>mdw-vignette.pdf.asis</r:source><r:filename>mdw-vignette.pdf</r:filename><r:title>Maximum Diversity Weighting</r:title><r:created>2025-07-27 18:26:42</r:created><r:modified>2025-07-27 18:26:42</r:modified></r:article></item><item><title>[youyifong] FSDAM 2024.7-30</title><author>youyifong@gmail.com (Youyi Fong)</author><description>FS-DAM performs feature extraction through latent
variables identification. Implementation is based on
autoencoders with monotonicity and orthogonality constraints.</description><link>https://github.com/r-universe/youyifong/actions/runs/24277426822</link><pubDate>Wed, 23 Jul 2025 19:40:17 GMT</pubDate><r:package>FSDAM</r:package><r:version>2024.7-30</r:version><r:status>failure</r:status><r:repository>https://youyifong.r-universe.dev</r:repository><r:upstream>https://github.com/youyifong/fsdam</r:upstream><r:article><r:source>FSDAM-vignette.pdf.asis</r:source><r:filename>FSDAM-vignette.pdf</r:filename><r:title>Forward Stepwise Deep Autoencoder-based Montone Nonlinear Dimensionality Reduction</r:title><r:created>2025-07-23 17:26:47</r:created><r:modified>2025-07-23 17:26:47</r:modified></r:article></item><item><title>[youyifong] chngpt 2024.11-15</title><author>youyifong@gmail.com (Youyi Fong)</author><description>Threshold regression models are also called two-phase
regression, broken-stick regression, split-point regression,
structural change models, and regression kink models, with and
without interaction terms. Methods for both continuous and
discontinuous threshold models are included, but the support
for the former is much greater. This package is described in
Fong, Huang, Gilbert and Permar (2017)
&lt;DOI:10.1186/s12859-017-1863-x&gt; and the package vignette.</description><link>https://github.com/r-universe/youyifong/actions/runs/24277556530</link><pubDate>Wed, 15 Jan 2025 17:45:43 GMT</pubDate><r:package>chngpt</r:package><r:version>2024.11-15</r:version><r:status>success</r:status><r:repository>https://youyifong.r-universe.dev</r:repository><r:upstream>https://github.com/youyifong/chngpt</r:upstream><r:article><r:source>chngpt-vignette.pdf.asis</r:source><r:filename>chngpt-vignette.pdf</r:filename><r:title>Fitting Threshold Regression Models Using chngpt</r:title><r:created>2021-02-04 23:01:16</r:created><r:modified>2021-02-04 23:01:16</r:modified></r:article></item><item><title>[youyifong] copcor 2024.7-31</title><author>youyifong@gmail.com (Youyi Fong)</author><description>Correlates of protection (CoP) and correlates of risk
(CoR) study the immune biomarkers associated with an infectious
disease outcome, e.g. COVID or HIV-1 infection. This package
contains shared functions for analyzing CoP and CoR, including
bootstrapping procedures, competing risk estimation, and
bootstrapping marginalized risks.</description><link>https://github.com/r-universe/youyifong/actions/runs/24277386277</link><pubDate>Thu, 01 Aug 2024 02:49:50 GMT</pubDate><r:package>copcor</r:package><r:version>2024.7-31</r:version><r:status>success</r:status><r:repository>https://youyifong.r-universe.dev</r:repository><r:upstream>https://github.com/cran/copcor</r:upstream><r:article><r:source>copcor-vignette.pdf.asis</r:source><r:filename>copcor-vignette.pdf</r:filename><r:title>Correlates of Protection Correlates of Risk</r:title><r:created>2023-08-30 16:34:40</r:created><r:modified>2023-08-30 16:34:40</r:modified></r:article></item><item><title>[youyifong] robustrank 2024.1-28</title><author>youyifong@gmail.com (Youyi Fong)</author><description>Implements two-sample tests for paired data with missing
values (Fong, Huang, Lemos and McElrath 2018, Biostatics,
&lt;doi:10.1093/biostatistics/kxx039&gt;) and modified
Wilcoxon-Mann-Whitney two sample location test, also known as
the Fligner-Policello test.</description><link>https://github.com/r-universe/youyifong/actions/runs/24277478589</link><pubDate>Mon, 29 Jan 2024 02:30:12 GMT</pubDate><r:package>robustrank</r:package><r:version>2024.1-28</r:version><r:status>success</r:status><r:repository>https://youyifong.r-universe.dev</r:repository><r:upstream>https://github.com/cran/robustrank</r:upstream></item><item><title>[youyifong] krm 2022.10-17</title><author>youyifong@gmail.com (Youyi Fong)</author><description>Implements several methods for testing the variance
component parameter in regression models that contain
kernel-based random effects, including a maximum of adjusted
scores test. Several kernels are supported, including a profile
hidden Markov model mutual information kernel for protein
sequence. This package is described in Fong et al. (2015)
&lt;DOI:10.1093/biostatistics/kxu056&gt;.</description><link>https://github.com/r-universe/youyifong/actions/runs/24277438683</link><pubDate>Tue, 18 Oct 2022 06:40:11 GMT</pubDate><r:package>krm</r:package><r:version>2022.10-17</r:version><r:status>success</r:status><r:repository>https://youyifong.r-universe.dev</r:repository><r:upstream>https://github.com/cran/krm</r:upstream></item></channel></rss>