Package: highMLR
Title: Machine Learning Feature Selection for High Dimensional Survival
        Data
Version: 1.0.1
Date: 2026-05-23
Authors@R: 
    person("Atanu", "Bhattacharjee", email = "atanustat@gmail.com",
           role = c("aut", "cre"))
Description: 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>.
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
License: GPL-3
Encoding: UTF-8
Language: en-GB
LazyData: true
LazyDataCompression: xz
RoxygenNote: 7.3.3
VignetteBuilder: knitr
Config/testthat/edition: 3
NeedsCompilation: no
Author: Atanu Bhattacharjee [aut, cre]
Maintainer: Atanu Bhattacharjee <atanustat@gmail.com>
Packaged: 2026-05-23 12:14:26 UTC; atanu
Repository: CRAN
Date/Publication: 2026-05-23 12:30:02 UTC
Built: R 4.6.0; ; 2026-05-25 04:38:36 UTC; unix
