survML1.2.0 package

Tools for Flexible Survival Analysis Using Machine Learning

crossfit_oracle_preds

Generate cross-fitted oracle prediction function estimates

crossfit_surv_preds

Generate cross-fitted conditional survival predictions

currstatCIR

Estimate a survival function under current status sampling

DR_pseudo_outcome_regression

Generate oracle prediction function estimates using doubly-robust pseu...

generate_folds

Generate cross-fitting and sample-splitting folds

predict.stackG

Obtain predicted conditional survival and cumulative hazard functions ...

predict.stackL

Obtain predicted conditional survival function from a local survival s...

stackG

Estimate a conditional survival function using global survival stackin...

stackL

Estimate a conditional survival function via local survival stacking

survML-package

survML: Tools for Flexible Survival Analysis Using Machine Learning

vim_accuracy

Estimate classification accuracy VIM

vim_AUC

Estimate AUC VIM

vim_brier

Estimate Brier score VIM

vim_cindex

Estimate concordance index VIM

vim_rsquared

Estimate R-squared (proportion of explained variance) VIM based on eve...

vim_survival_time_mse

Estimate restricted predicted survival time MSE VIM

vim

Estimate AUC VIM

Download source packageRead PDF manual

Statistical tools for analyzing time-to-event data using machine learning. Implements survival stacking for conditional survival estimation, standardized survival function estimation for current status data, and methods for algorithm-agnostic variable importance. See Wolock CJ, Gilbert PB, Simon N, and Carone M (2024) <doi:10.1080/10618600.2024.2304070>.

  • Maintainer: Charles Wolock
  • License: GPL (>= 3)
  • Last published: 2024-10-31

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