chestR: Kernel-Weighted Cox Regression for Treatment Effect Heterogeneity

Explores treatment effect heterogeneity and candidate predictive biomarkers by re-fitting weighted Cox proportional hazards models on a biomarker grid. Kernel weights centred at each grid point produce local coefficient estimates that can be visualised across biomarker space. Builds on ideas related to graphical Cox treatment-covariate interaction methods [see Bonetti and Gelber (2004) <doi:10.1093/biostatistics/kxh002> and local partial-likelihood approaches Fan, Lin and Zhou (2006) <doi:10.1214/009053605000000796>].

Version:	0.1.0
Imports:	survival, ggplot2, scales
Suggests:	knitr, rmarkdown, mvtnorm, testthat (≥ 3.0.0)
Published:	2026-09-12
DOI:	10.32614/CRAN.package.chestR (may not be active yet)
Author:	Richard Jackson [aut, cre], Caroline Jeffery [aut]
Maintainer:	Richard Jackson <richj23 at liverpool.ac.uk>
License:	MIT + file LICENSE
NeedsCompilation:	no
Language:	en-GB
Materials:	README, NEWS
CRAN checks:	chestR results

Documentation:

Reference manual:	chestR.html , chestR.pdf
Vignettes:	chestR workflow: global Cox to local biomarker maps (source, R code)

Downloads:

Package source:	chestR_0.1.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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