R: Adaptive Approaches for Signal Detection in Pharmacovigilance

# Adaptive Approaches for Signal Detection in Pharmacovigilance

## Documentation for package ‘adapt4pv’ version 0.2-3

- DESCRIPTION file
- Package NEWS

## Help Pages

adapt4pv-package | Adaptive approaches for signal detection in PharmacoVigilance
adapt_bic | fit an adaptive lasso with adaptive weights derived from lasso-bic
adapt_cisl | fit an adaptive lasso with adaptive weights derived from CISL
adapt_cv | fit an adaptive lasso with adaptive weights derived from lasso-cv
adapt_univ | fit an adaptive lasso with adaptive weights derived from univariate coefficients
cisl | Class Imbalanced Subsampling Lasso
data_PV | Simulated data for the adapt4pv package
est_ps_bic | propensity score estimation in high dimension with automated covariates selection using lasso-bic
est_ps_hdps | propensity score estimation in high dimension with automated covariates selection using hdPS
est_ps_xgb | propensity score estimation in high dimension using gradient tree boosting
lasso_bic | fit a lasso regression and use standard BIC for variable selection
lasso_cv | wrap function for 'cv.glmnet'
lasso_perm | fit a lasso regression and use standard permutation of the outcome for variable selection
ps_adjust | adjustment on propensity score
ps_adjust_one | adjustment on propensity score for one drug exposure
ps_pond | weihting on propensity score
ps_pond_one | weihting on propensity score for one drug exposure
summary_stat | Summary statistics for main adapt4pv package functions
X | Simulated data for the adapt4pv package
Y | Simulated data for the adapt4pv package