Adaptive Approaches for Signal Detection in Pharmacovigilance

Documentation for package ‘adapt4pv’ version 0.2-3

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