Machine Learning Feature Selection for High Dimensional Survival Data R package highMLR version 1.0.1 A unified, flexible framework for high dimensional feature selection in the presence of a survival outcome. Provides multiple machine learning approaches (Cox elastic net, random survival forest, accelerated oblique random survival forest, gradient-boosted Cox, stability selection, classical univariate Cox screening, pseudo- observation bridging to arbitrary regression learners, and Fine-Gray competing risks selection) under a single interface. Adds causal survival forest estimation of heterogeneous treatment effects on survival (experimental), conformal survival prediction with finite- sample coverage guarantees, and time-dependent 'SHAP' explanations via 'SurvSHAP(t)'. Methodology is based on regularised Cox regression (2011) <doi:10.18637/jss.v039.i05>, random survival forests (2008) <doi:10.1214/08-AOAS169>, oblique random survival forests (2024) <doi:10.1080/10618600.2023.2231048>, stability selection (2010) <doi:10.1111/j.1467-9868.2010.00740.x>, causal survival forests (2023) <doi:10.1111/rssb.12538>, time-dependent survival explanations (2023) <doi:10.1016/j.knosys.2022.110234>, conformal survival prediction (2023) <doi:10.1093/biomet/asad043>, the Fine-Gray model for competing risks (1999) <doi:10.1080/01621459.1999.10474144>, and pseudo-observation regression (2010) <doi:10.1177/0962280209105020>. Software Depends: R (>= 4.1.0) Imports: survival, glmnet, ranger, aorsf, xgboost, stabs, survex, grf, prodlim, cmprsk, future, future.apply, tibble, ggplot2, rlang, stats, utils Suggests: knitr, rmarkdown, testthat (>= 3.0.0), mice, riskRegression Atanu Bhattacharjee <atanustat@gmail.com> Comprehensive R Archive Network (CRAN) Atanu Bhattacharjee [aut, cre] GPL-3 2026-05-23 application/tgz https://CRAN.R-project.org/package=highMLR doi:10.32614/CRAN.package.highMLR en-GB