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
