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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 |
| Reference manual: | highMLR.html , highMLR.pdf |
| Vignettes: |
Getting started with highMLR (source, R code) |
| Package source: | highMLR_1.0.1.tar.gz |
| Windows binaries: | r-devel: highMLR_1.0.1.zip, r-release: highMLR_1.0.1.zip, r-oldrel: highMLR_1.0.1.zip |
| macOS binaries: | r-release (arm64): highMLR_1.0.1.tgz, r-oldrel (arm64): highMLR_1.0.1.tgz, r-release (x86_64): highMLR_1.0.1.tgz, r-oldrel (x86_64): highMLR_1.0.1.tgz |
| Old sources: | highMLR archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.