Package: WMAP 1.0.0

Subharup Guha

WMAP: Weighted Meta-Analysis with Pseudo-Populations

Implementation of integrative weighting approaches for multiple observational studies and causal inferences. The package features three weighting approaches, each representing a special case of the unified weighting framework, introduced by Guha and Li (2024) <doi:10.1093/biomtc/ujae070>, which includes an extension of inverse probability weights for data integration settings.

Authors:Subharup Guha [aut, cre], Mengqi Xu [aut], Kashish Priyam [aut], Yi Li [aut]

WMAP_1.0.0.tar.gz
WMAP_1.0.0.tar.gz(r-4.5-noble)WMAP_1.0.0.tar.gz(r-4.4-noble)
WMAP_1.0.0.tgz(r-4.4-emscripten)WMAP_1.0.0.tgz(r-4.3-emscripten)
WMAP.pdf |WMAP.html
WMAP/json (API)

# Install 'WMAP' in R:
install.packages('WMAP', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:

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

1.00 score 2 exports 80 dependencies

Last updated 16 days agofrom:b90a600502. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 08 2024
R-4.5-linuxOKNov 08 2024

Exports:balancing.weightscausal.estimate

Dependencies:assertthatcaretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverforcatsforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgcondpkgconfigplyrpROCprodlimprogressrproxypurrrR6randomForestRColorBrewerRcpprecipesreshape2rlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithrzeallot