highMLR 1.0.0

Machine learning feature selection for high dimensional survival data.

What's new in 1.0.0

Complete rewrite, superseding the original CRAN release v0.1.1. v0.1.1 offered six hardcoded functions, each fitting a single Cox-based model in a feature-by-feature loop. This version replaces that with a single, flexible entry point that dispatches to modern ML backends:

Method	Backend	Notes
coxnet	glmnet (Cox EN)	Fast; baseline for p >> n
rsf	ranger	Permutation importance
aorsf	aorsf	Accelerated oblique RSF (Jaeger 2024)
xgboost	xgboost	Gradient-boosted Cox
stability	stabs + glmnet	Stability selection with PFER control
univariate	survival::coxph	v0.1.x-style screening baseline
pseudo	pseudo-observations	Bridge to any regression learner
finegray	cmprsk / survival	Competing-risks subdistribution hazard

Plus companions: highmlr_compare(), highmlr_stability(), highmlr_explain() (time-dependent SHAP via survex), highmlr_screen(), highmlr_report(), and the additional tools highmlr_causal() (causal survival forest via grf, experimental) and highmlr_conformal() (conformal survival prediction intervals).

Install (development)

# install.packages("remotes")
remotes::install_local("path/to/highMLR")

Quick start

library(highMLR)
data(hnscc)

# One-liner: Cox elastic net with 5-fold CV
fit <- highmlr(hnscc, time = "OS", status = "Death", method = "coxnet")
print(fit)
plot(fit)

# Compare three methods
cmp <- highmlr_compare(hnscc, "OS", "Death",
                       methods = c("coxnet", "rsf", "xgboost"))
cmp$summary

Breaking changes from 0.1.1

All v0.1.1 functions (mlhighCox, mlhighKap, mlhighFrail, mlhighHet, mlclassCox, mlclassKap) are removed. Use highmlr() with the appropriate method argument. The univariate method reproduces the v0.1.x screening behaviour.