Adaptive Approaches for Signal Detection in Pharmacovigilance


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Documentation for package ‘adapt4pv’ version 0.1.0

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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_ridge fit an adaptive lasso with adaptive weights derived from ridge regression
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