Package: adapt4pv 0.2-3

Emeline Courtois

adapt4pv: Adaptive Approaches for Signal Detection in Pharmacovigilance

A collection of several pharmacovigilance signal detection methods based on adaptive lasso. Additional lasso-based and propensity score-based signal detection approaches are also supplied. See Courtois et al <doi:10.1186/s12874-021-01450-3>.

Authors:Emeline Courtois [cre], Ismaïl Ahmed [aut], Hervé Perdry [ctb]

adapt4pv_0.2-3.tar.gz
adapt4pv_0.2-3.tar.gz(r-4.5-noble)adapt4pv_0.2-3.tar.gz(r-4.4-noble)
adapt4pv_0.2-3.tgz(r-4.4-emscripten)adapt4pv_0.2-3.tgz(r-4.3-emscripten)
adapt4pv.pdf |adapt4pv.html✨
adapt4pv/json (API)
NEWS

# Install 'adapt4pv' in R:

install.packages('adapt4pv', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

X - Simulated data for the adapt4pv package
Y - Simulated data for the adapt4pv package

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

16 exports 1 stars 0.00 score 18 dependencies 491 downloads

Last updated 1 years agofrom:3ccd2aecc1. Checks:OK: 2. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Sep 14 2024
R-4.5-linux	OK	Sep 14 2024

Exports:adapt_bic adapt_cisl adapt_cv adapt_univ cisl est_ps_bic est_ps_hdps est_ps_xgb lasso_bic lasso_cv lasso_perm ps_adjust ps_adjust_one ps_pond ps_pond_one summary_stat

Dependencies:biglm codetools data.table DBI doParallel foreach glmnet iterators jsonlite lattice MASS Matrix Rcpp RcppEigen shape speedglm survival xgboost

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Readme and manuals

Help Manual

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

Package: adapt4pv 0.2-3

adapt4pv: Adaptive Approaches for Signal Detection in Pharmacovigilance

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)