R: Nested Cross-Validation to Compare Cox-PH, Cox-Lasso, Survival Random Forests

# Nested Cross-Validation to Compare Cox-PH, Cox-Lasso, Survival Random Forests

## Documentation for package ‘survcompare’ version 0.3.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

"_PACKAGE" | Cross-validates and compares Cox Proportionate Hazards and Survival Random Forest models
linear_beta | Auxiliary function for simulatedata functions
ml_hyperparams_srf | Internal function for getting grid of hyperparameters for random or grid search of size = max_grid_size
print.survcompare | Print survcompare object
print.survensemble_cv | Prints trained survensemble 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
summary.survcompare | Summary of survcompare results
summary.survensemble_cv | Prints summary of a trained survensemble_cv object
survcompare | Cross-validates and compares Cox Proportionate Hazards and Survival Random Forest models
survcompare2 | Compares two cross-validated models using surv____cv functions of this package.
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
survival_prob_km | Calculates survival probability estimated by Kaplan-Meier survival curve Uses polynomial extrapolation in survival function space, using poly(n=3)
survsrfens_cv | Cross-validates predictive performance for SRF Ensemble
survsrfens_predict | Predicts event probability by a trained sequential ensemble of Survival Random Forest and CoxPH
survsrfens_train | Fits an ensemble of Cox-PH and Survival Random Forest (SRF) with internal CV to tune SRF hyperparameters.
survsrfstack_cv | Cross-validates stacked ensemble of the CoxPH and Survival Random Forest models
survsrfstack_predict | Predicts event probability by a trained stacked ensemble of Survival Random Forest and CoxPH
survsrfstack_train | Trains the stacked ensemble of the CoxPH and Survival Random Forest
survsrf_cv | Cross-validates Survival Random Forest
survsrf_predict | Predicts event probability by a trained Survival Random Forest
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 | A repeated 3-fold CV over a hyperparameters grid
survsrf_tune_single | Internal function for survsrf_tune(), performs 1 CV
surv_brierscore | Calculates time-dependent Brier Score
surv_validate | Computes performance statistics for a survival data given the predicted event probabilities