highMLR-package: highMLR: Machine Learning Feature Selection for High...

In highMLR: Machine Learning Feature Selection for High Dimensional Survival Data

highMLR: Machine Learning Feature Selection for High Dimensional Survival Data

Description

A unified, flexible framework for high dimensional feature selection in the presence of a survival outcome. Provides multiple machine learning approaches under a single interface: Cox elastic net, random survival forest, accelerated oblique RSF, gradient-boosted Cox, stability selection, classical univariate Cox screening, pseudo-observation bridging to any regression learner, and Fine-Gray competing risks selection. Adds causal survival forest estimation of heterogeneous treatment effects, conformal survival prediction intervals, and time-dependent SHAP explanations via SurvSHAP(t).

Main functions

[highmlr()]

Main entry point. Fit one of eight ML methods.

[highmlr_compare()]

Compare multiple methods side by side.

[highmlr_stability()]

Stability selection wrapper.

[highmlr_explain()]

Time-dependent SHAP via SurvSHAP(t).

[highmlr_screen()]

Pre-screening for very high p.

[highmlr_report()]

Generate a Quarto/Rmd report.

[highmlr_causal()]

Causal survival forest (experimental).

[highmlr_conformal()]

Conformal prediction intervals.

Bundled datasets

[hnscc]

High dimensional head and neck cancer survival data.

[srdata]

High dimensional protein gene expression data.

Author(s)

Atanu Bhattacharjee [e-mail]

highMLR documentation built on May 23, 2026, 5:07 p.m.