highMLR

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>.

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Package required but not available: ‘survex’

Packages suggested but not available for checking: 'mice', 'riskRegression'

See section ‘The DESCRIPTION file’ in the ‘Writing R Extensions’
manual.

Maintainer: ‘Atanu Bhattacharjee <atanustat@gmail.com>’

The Description field contains
  <arXiv:2012.02102>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Atanu Bhattacharjee <atanustat@gmail.com>’

The Description field contains
  <arXiv:2012.02102>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

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