Package: highMLR 1.0.1

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>.

Authors:Atanu Bhattacharjee [aut, cre]