Kernel-Weighted Cox Regression for Treatment Effect Heterogeneity R package chestR version 0.1.0 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>]. Software Imports: survival, ggplot2, scales Suggests: knitr, rmarkdown, mvtnorm, testthat (>= 3.0.0) Richard Jackson <richj23@liverpool.ac.uk> Comprehensive R Archive Network (CRAN) Richard Jackson [aut, cre], Caroline Jeffery [aut] MIT + file LICENSE (https://CRAN.R-project.org/package=chestR/LICENSE) 2026-09-12 application/tgz https://CRAN.R-project.org/package=chestR doi:10.32614/CRAN.package.chestR en-GB