R: Benchmarking and Visualization Toolkit for Penalized Cox Models

# Benchmarking and Visualization Toolkit for Penalized Cox Models

## Documentation for package ‘hdnom’ version 6.2.0

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

## Help Pages

as_nomogram | Construct nomogram ojects for high-dimensional Cox models
calibrate | Calibrate high-dimensional Cox models
calibrate_external | Externally calibrate high-dimensional Cox models
compare_by_calibrate | Compare high-dimensional Cox models by model calibration
compare_by_validate | Compare high-dimensional Cox models by model validation
fit_aenet | Model selection for high-dimensional Cox models with adaptive elastic-net penalty
fit_alasso | Model selection for high-dimensional Cox models with adaptive lasso penalty
fit_enet | Model selection for high-dimensional Cox models with elastic-net penalty
fit_flasso | Model selection for high-dimensional Cox models with fused lasso penalty
fit_lasso | Model selection for high-dimensional Cox models with lasso penalty
fit_mcp | Model selection for high-dimensional Cox models with MCP penalty
fit_mnet | Model selection for high-dimensional Cox models with Mnet penalty
fit_scad | Model selection for high-dimensional Cox models with SCAD penalty
fit_snet | Model selection for high-dimensional Cox models with Snet penalty
glmnet_basesurv | Breslow baseline hazard estimator for glmnet objects
glmnet_survcurve | Survival curve prediction for glmnet objects
infer_variable_type | Extract information of selected variables from high-dimensional Cox models
kmplot | Kaplan-Meier plot with number at risk table for internal calibration and external calibration results
logrank_test | Log-rank test for internal calibration and external calibration results
ncvreg_basesurv | Breslow baseline hazard estimator for ncvreg objects
ncvreg_survcurve | Survival curve prediction for ncvreg objects
penalized_basesurv | Breslow baseline hazard estimator for penfit objects
penalized_survcurve | Survival curve prediction for penfit objects
plot.hdnom.calibrate | Plot calibration results
plot.hdnom.calibrate.external | Plot external calibration results
plot.hdnom.compare.calibrate | Plot model comparison by calibration results
plot.hdnom.compare.validate | Plot model comparison by validation results
plot.hdnom.nomogram | Plot nomogram objects
plot.hdnom.validate | Plot optimism-corrected time-dependent discrimination curves for validation
plot.hdnom.validate.external | Plot time-dependent discrimination curves for external validation
predict.hdnom.model | Make predictions from high-dimensional Cox models
print.hdnom.calibrate | Print calibration results
print.hdnom.calibrate.external | Print external calibration results
print.hdnom.compare.calibrate | Print model comparison by calibration results
print.hdnom.compare.validate | Print model comparison by validation results
print.hdnom.model | Print high-dimensional Cox model objects
print.hdnom.nomogram | Print nomograms objects
print.hdnom.validate | Print validation results
print.hdnom.validate.external | Print external validation results
smart | Imputed SMART study data
smarto | Original SMART study data
summary.hdnom.calibrate | Summary of calibration results
summary.hdnom.calibrate.external | Summary of external calibration results
summary.hdnom.compare.calibrate | Summary of model comparison by calibration results
summary.hdnom.compare.validate | Summary of model comparison by validation results
summary.hdnom.validate | Summary of validation results
summary.hdnom.validate.external | Summary of external validation results
theme_hdnom | Plot theme (ggplot2) for hdnom
validate | Validate high-dimensional Cox models with time-dependent AUC
validate_external | Externally validate high-dimensional Cox models with time-dependent AUC