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: 2026-06-22 11:02:01 UTC; root
Config/pak/sysreqs: cmake make libicu-dev libuv1-dev
Repository: https://atanubhattacharjee.r-universe.dev
Date/Publication: 2026-05-23 12:30:02 UTC
RemoteUrl: https://github.com/cran/highMLR
RemoteRef: HEAD
RemoteSha: 80aa21d0f9de3c6ae8e7b508e42b122b9b32dbe8