NEWS.md

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

highMLR 1.0.1

• Default feature set in highmlr() now excludes non-numeric columns (e.g. identifiers) automatically, preventing an error when features is left unspecified.
• The coxnet backend now validates that supplied features are numeric and reports a clear error otherwise.
• Learner backends (rsf, aorsf, xgboost) now default to at most two threads, configurable via options(), to respect multi-core policies in batch-checking environments.

highMLR 1.0.0

Major rewrite. This release supersedes the original CRAN version 0.1.1 and is not backward-compatible with it. The previous six functions (mlhighCox, mlhighKap, mlhighFrail, mlhighHet, mlclassCox, mlclassKap) and their mlr3/coxme/missForest backend have been replaced by a single, unified interface. Code written for 0.1.1 will not run unchanged.

New unified interface

• Single highmlr() entry point dispatching to eight feature-selection methods, all returning a common highmlr_fit S3 object: Cox elastic net (coxnet), random survival forest (rsf), accelerated oblique RSF (aorsf), gradient-boosted Cox (xgboost), stability selection (stability), univariate Cox screening (univariate), pseudo-observation bridging (pseudo), and Fine-Gray competing-risks selection (finegray).
• Common verbs for the fit object: print(), summary(), plot(), coef(), predict().
• Companion functions: highmlr_compare(), highmlr_stability(), highmlr_explain(), highmlr_screen(), highmlr_report().

Feature-selection methods

• method = "pseudo" -- Andersen & Perme (2010) pseudo-observation bridge. Computes jackknife pseudo-values for S(t) at chosen evaluation times, then fits any regression learner (ranger, xgboost, glmnet) on the transformed outcome, allowing arbitrary regression ML on right-censored data without the proportional hazards assumption.
• method = "finegray" -- Fine-Gray subdistribution hazard feature selection for competing-risks data. Status coded 0/1/2+ (cause of interest configurable). Returns subdistribution hazard ratios with FDR-adjusted p-values.

Causal, conformal, and explanation tools

• highmlr_causal() -- EXPERIMENTAL. Causal survival forest via grf for heterogeneous treatment effects on survival. Returns per-patient CATE estimates (RMST or survival-probability difference) with standard errors, the average treatment effect, and covariate importance.
• highmlr_conformal() -- conformal prediction intervals for survival time with finite-sample marginal coverage (Candes, Lei & Ren, 2023), using inverse-probability-of-censoring weighting on the calibration set.
• highmlr_explain() -- time-dependent SHAP via SurvSHAP(t) (Krzyzinski et al., 2023) by default, returning SHAP values per feature, per patient, across time, with aggregated mean |SHAP| importance. Permutation and break-down methods are also available.

Dependencies

• Imports: survival, glmnet, ranger, aorsf, xgboost, stabs, survex, grf, prodlim, cmprsk, future, future.apply, tibble, ggplot2, rlang, stats, utils.
• Suggests: knitr, rmarkdown, testthat, mice, riskRegression.
• The mlr3, mlr3learners, gtools, dplyr, coxme, missForest, and R6 dependencies from 0.1.1 are no longer used.

Notes

• Neural network methods (DeepSurv, DeepHit) are deferred to a future release to keep this version free of torch/Python dependencies.
• Datasets hnscc and srdata are retained from 0.1.1, now with expanded and corrected documentation.

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