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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(mlhighCox, mlhighKap, mlhighFrail, mlhighHet, mlclassCox, mlclassKap) and their mlr3/coxme/missForest backend have been replaced by a single, unified interface.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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.