highMLR: Machine Learning Feature Selection for High Dimensional Survival
Data
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>.
| Version: |
1.0.1 |
| 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 |
| Published: |
2026-05-23 |
| DOI: |
10.32614/CRAN.package.highMLR |
| Author: |
Atanu Bhattacharjee [aut, cre] |
| Maintainer: |
Atanu Bhattacharjee <atanustat at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Language: |
en-GB |
| Materials: |
README, NEWS |
| CRAN checks: |
highMLR results |
Documentation:
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