R: Estimation and Validation Methods for Subgroup Identification and Personalized Medicine

# Estimation and Validation Methods for Subgroup Identification and Personalized Medicine

## Documentation for package ‘personalized’ version 0.2.8

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

## Help Pages

check.overlap | Check propensity score overlap
create.augmentation.function | Creation of augmentation functions
create.propensity.function | Creation of propensity fitting function
fit.subgroup | Fitting subgroup identification models
LaLonde | National Supported Work Study Data
plot.subgroup_fitted | Plotting results for fitted subgroup identification models
plot.subgroup_validated | Plotting results for fitted subgroup identification models
plotCompare | Plot a comparison results for fitted or validated subgroup identification models
predict.subgroup_fitted | Function to predict either benefit scores or treatment recommendations
predict.wksvm | Function to predict either benefit scores or treatment recommendations
print.individual_treatment_effects | Printing individualized treatment effects
print.subgroup_fitted | Printing results for fitted subgroup identification models
print.subgroup_summary | Printing results for fitted subgroup identification models
print.subgroup_validated | Printing results for fitted subgroup identification models
subgroup.effects | Computes treatment effects within various subgroups
summarize.subgroups | Summarizing covariates within estimated subgroups
summarize.subgroups.default | Summarizing covariates within estimated subgroups
summarize.subgroups.subgroup_fitted | Summarizing covariates within estimated subgroups
summary.subgroup_fitted | Summary of results for fitted subgroup identification models
summary.wksvm | Summary of results for fitted subgroup identification models
treat.effects | Calculation of covariate-conditional treatment effects
treatment.effects | Calculation of covariate-conditional treatment effects
treatment.effects.default | Calculation of covariate-conditional treatment effects
treatment.effects.subgroup_fitted | Calculation of covariate-conditional treatment effects
validate.subgroup | Validating fitted subgroup identification models
weighted.ksvm | Fit weighted kernel svm model.