| cox_calibration_stats | Calibration stats of a fitted Cox PH model |
| linear_beta | Auxiliary function for simulatedata functions |
| predict.survensemble | Predicts event probability for a fitted survensemble |
| print.survcompare | Print survcompare object |
| print.survensemble | Prints trained survensemble object |
| print.survensemble_cv | Prints survensemble_cv object |
| simulate_crossterms | Simulated sample with survival outcomes with non-linear and cross-term dependencies |
| simulate_linear | Simulated sample with survival outcomes with linear dependencies |
| simulate_nonlinear | Simulated sample with survival outcomes with non-linear dependencies |
| srf_survival_prob_for_time | Internal function to compute survival probability by time from a fitted survival random forest |
| summary.survcompare | Summary of survcompare results |
| summary.survensemble | Prints summary of a trained survensemble object |
| summary.survensemble_cv | Prints a summary of survensemble_cv object |
| survcompare | Cross-validates and compares Cox Proportionate Hazards and Survival Random Forest models |
| survcoxlasso_train | Trains CoxLasso, using cv.glmnet(s="lambda.min") |
| survcox_cv | Cross-validates Cox or CoxLasso model |
| survcox_predict | Computes event probabilities from a trained cox model |
| survcox_train | Trains CoxPH using survival package, or trains CoxLasso (cv.glmnet, lambda.min), and then re-trains survival:coxph on non-zero predictors |
| survensemble_cv | Cross-validates predictive performance for Ensemble 1 |
| survensemble_train | Fits an ensemble of Cox-PH and Survival Random Forest (SRF) with internal CV to tune SRF hyperparameters. |
| survival_prob_km | Calculates survival probability estimated by Kaplan-Meier survival curve Uses polynomial extrapolation in survival function space, using poly(n=3) |
| survsrf_cv | Cross-validates SRF model |
| survsrf_predict | Predicts event probability for a fitted SRF model |
| survsrf_train | Fits randomForestSRC, with tuning by mtry, nodedepth, and nodesize. Underlying model is by Ishwaran et al(2008) https://www.randomforestsrc.org/articles/survival.html Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. The Annals of Applied Statistics. 2008;2:841–60. |
| survsrf_tune | Internal function to tune SRF model, in nested CV loop |
| surv_brierscore | Calculates time-dependent Brier Score |
| surv_validate | Computes performance statistics for a survival data given the predicted event probabilities |