highmlr() now excludes non-numeric columns
(e.g. identifiers) automatically, preventing an error when features
is left unspecified.coxnet backend now validates that supplied features are numeric
and reports a clear error otherwise.rsf, aorsf, xgboost) now default to at most two
threads, configurable via options(), to respect multi-core policies
in batch-checking environments.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mlclassKap) and their mlr3/coxme/missForest backend have been
replaced by a single, unified interface.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.
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).print(), summary(), plot(),
coef(), predict().highmlr_compare(), highmlr_stability(),
highmlr_explain(), highmlr_screen(), highmlr_report().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.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.survival, glmnet, ranger, aorsf, xgboost, stabs,
survex, grf, prodlim, cmprsk, future, future.apply,
tibble, ggplot2, rlang, stats, utils.knitr, rmarkdown, testthat, mice, riskRegression.mlr3, mlr3learners, gtools, dplyr, coxme, missForest,
and R6 dependencies from 0.1.1 are no longer used.hnscc and srdata are retained from 0.1.1, now with
expanded and corrected documentation.