# High-Dimensional Cure Models

## Documentation for package ‘hdcuremodels’ version 0.0.5

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

## Help Pages

amltest | AML test data
amltrain | AML training data
auc_mcm | AUC for cure prediction using mean score imputation
coef.mixturecure | Extract model coefficients from a fitted mixturecure object
coefficients | Extract model coefficients from a fitted mixturecure object
concordance_mcm | C-statistic for mixture cure models
cureem | Fit penalized mixture cure model using the E-M algorithm
curegmifs | Fit penalized parametric mixture cure model using the GMIFS algorithm
cure_estimate | Estimate cured fraction
cv_cureem | Fit penalized mixture cure model using the E-M algorithm with cross-validation for parameter tuning
cv_curegmifs | Fit a penalized parametric mixture cure model using the generalized monotone incremental forward stagewise (GMIFS) algorithm (Hastie et al 2007) with cross-validation for model selection
dim.mixturecure | Dimension method for mixturecure objects
family.mixturecure | Return model family and fitting algorithm for mixturecure model fits
formula.mixturecure | Extract model formula for mixturecure object
generate_cure_data | Simulate data under a mixture cure model
logLik.mixturecure | Log-likelihood for fitted mixture cure model
nobs.mixturecure | Number of observations in mixturecure object
nonzerocure_test | Non-parametric test for a non-zero cured fraction
npar_mixturecure | Number of parameters in fitted mixture cure model
plot.mixturecure | Plot fitted mixture cure model
predict.mixturecure | Predicted probabilities for susceptibles, linear predictor for latency, and risk class for latency for mixture cure fit
print.mixturecure | Print the contents of a mixture cure fitted object
sufficient_fu_test | Test for sufficient follow-up
summary.mixturecure | Summarize a fitted mixture cure object