Package: WMAP 1.1.0
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:
WMAP_1.1.0.tar.gz
WMAP_1.1.0.tar.gz(r-4.5-noble)WMAP_1.1.0.tar.gz(r-4.4-noble)
WMAP_1.1.0.tgz(r-4.4-emscripten)WMAP_1.1.0.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')) |
- S - Demo Dataset
- X - Demo Dataset
- Y - Demo Dataset
- Z - Demo Dataset
- groupNames - Demo Dataset
- naturalGroupProp - Demo Dataset
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 24 days agofrom:f7dfd6c876. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 30 2024 |
| R-4.5-linux | OK | Nov 30 2024 |
Exports:balancing.weightscausal.estimateget_weightsmean_diffpercentESSsigma_ratio
Dependencies:assertthatcaretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverforcatsforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgcondpkgconfigplyrpROCprodlimprogressrproxypurrrR6randomForestRColorBrewerRcpprecipesreshape2rlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithrzeallot
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Compute balancing weights using FLEXOR or other methods | balancing.weights |
| Estimate causal effects using FLEXOR or other methods | causal.estimate |
| Demo Dataset | demo groupNames naturalGroupProp S X Y Z |
| Extract sample weights | get_weights |
| Extract causal estimates (mean differences) | mean_diff |
| Extract percentage sample ESS | percentESS |
| Boxplot of percent ESS | plot.causal_estimates |
| Print method for objects of class 'balancing_weights' | print.balancing_weights |
| Print method for objects of class 'causal_estimates' | print.causal_estimates |
| Extract sigma ratios | sigma_ratio |
| Summary method for objects of class 'balancing_weights' | summary.balancing_weights |
| Summary method for objects of class 'causal_estimates' | summary.causal_estimates |