<?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>larry-leon.r-universe.dev</title><link>https://larry-leon.r-universe.dev</link><description>Recent package updates in larry-leon</description><generator>R-universe</generator><image><url>https://github.com/larry-leon.png</url><title>R packages by larry-leon</title><link>https://larry-leon.r-universe.dev</link></image><lastBuildDate>Fri, 30 Oct 2026 02:39:17 GMT</lastBuildDate><item><title>[larry-leon] forestsearch 0.4.1</title><author>larry.leon.05@post.harvard.edu (Larry Leon)</author><description>Implements statistical methods for exploratory subgroup
identification in clinical trials. Provides tools for
identifying patient subgroups with differential treatment
effects using machine learning approaches including Generalized
Random Forests (GRF), LASSO regularization, and exhaustive
combinatorial search algorithms. Supports survival endpoints
(Cox proportional hazards), binary outcomes (log odds ratio,
log relative risk, risk difference), continuous outcomes (mean
difference), and count / rate outcomes (log incidence rate
ratio via Poisson, quasi-Poisson, or negative-binomial GLMs
with optional person-time offset). Features bootstrap bias
correction using infinitesimal jackknife methods to address
selection bias in post-hoc analyses. Designed for clinical
researchers conducting exploratory subgroup analyses in
randomized controlled trials, particularly for multi-regional
clinical trials (MRCT) requiring regional consistency
evaluation. Methods are described in Leon et al. (2024)
&lt;doi:10.1002/sim.10163&gt;. Post-selection inference for the
identified subgroup follows León and Anderson (2026)
&lt;doi:10.48550/arXiv.2609.38361&gt;. The search methodology is
given in León and Anderson (2026)
&lt;doi:10.48550/arXiv.2610.28379&gt;.</description><link>https://github.com/r-universe/larry-leon/actions/runs/41029484557</link><pubDate>Fri, 30 Oct 2026 02:39:17 GMT</pubDate><r:package>forestsearch</r:package><r:version>0.4.1</r:version><r:status>success</r:status><r:repository>https://larry-leon.r-universe.dev</r:repository><r:upstream>https://github.com/larry-leon/forestsearch</r:upstream><r:article><r:source>forestsearch.qmd</r:source><r:filename>forestsearch.html</r:filename><r:title>Interpretable subgroup identification with forestsearch</r:title><r:created>2026-02-10 01:14:42</r:created><r:modified>2026-10-28 20:44:19</r:modified></r:article></item><item><title>[larry-leon] weightedsurv 0.1.0</title><author>larry.leon.05@post.harvard.edu (Larry Leon)</author><description>Provides survival analysis functions with support for
time-dependent and subject-specific (e.g., propensity score)
weighting. Implements weighted estimation for Cox models,
Kaplan-Meier survival curves, and treatment differences with
point-wise and simultaneous confidence bands. Includes
restricted mean survival time (RMST) comparisons evaluated
across all potential truncation times with both point-wise and
simultaneous confidence bands. See Cole, S. R. &amp; Hernán, M. A.
(2004) &lt;doi:10.1016/j.cmpb.2003.10.004&gt; for methodological
background.</description><link>https://github.com/r-universe/larry-leon/actions/runs/40876297140</link><pubDate>Thu, 12 Feb 2026 18:28:14 GMT</pubDate><r:package>weightedsurv</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://larry-leon.r-universe.dev</r:repository><r:upstream>https://github.com/larry-leon/weightedsurv</r:upstream><r:article><r:source>weightedsurv_examples.Rmd</r:source><r:filename>weightedsurv_examples.html</r:filename><r:title>Weighted Survival Analysis Examples</r:title><r:created>2025-11-18 20:05:05</r:created><r:modified>2026-02-12 03:35:15</r:modified></r:article></item></channel></rss>