Introduction | Installation | Quick Start Example | Load Demo Data | Run Analysis | View Results | Understanding the Output | Advanced Features | Choosing a Weighting Method | Outcome Model | Cross-fitting | Bootstrap Options | Adjust Bootstrap Sample Size | Fast Point Estimates (No Bootstrap) | Parallel Bootstrap | Diagnostic Warnings | Low ESS Warning | Extreme Weights Warning | Positivity Violations | Common Workflows | Standard Analysis Workflow | Method Comparison Workflow | Sensitivity Analysis Workflow | Working with Weights Only | Getting Help
Package: WMAP 1.3.1
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_1.3.1.tar.gz
WMAP_1.3.1.tar.gz(r-4.7-any)WMAP_1.3.1.tar.gz(r-4.6-any)
WMAP_1.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
WMAP/json (API)
| # Install 'WMAP' in R: |
| install.packages('WMAP', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
- groupNames - Demo Dataset
- naturalGroupProp - Demo Dataset
- S - Demo Dataset
- X - Demo Dataset
- Y - Demo Dataset
- Z - Demo Dataset
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:c552512f42. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 149 | ||
| source / vignettes | OK | 183 | ||
| linux-release-x86_64 | OK | 147 | ||
| wasm-release | OK | 117 |
Exports:balancing.weightscausal.estimate
Dependencies:assertthatclicpp11farverforcatsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMatrixpillarpkgcondpkgconfigR6randomForestrangerRColorBrewerRcppRcppEigenrlangS7scalestibbleutf8vctrsviridisLitewithrzeallot
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Compute balancing weights using FLEXOR, IC, or IGO methods | balancing.weights |
| Estimate causal effects using FLEXOR, IC, or IGO methods | causal.estimate |
| Demo Dataset | demo groupNames naturalGroupProp S X Y Z |
| Plot method for objects of class 'causal_estimates' | plot.causal_estimates |
| Summary method for objects of class 'balancing_weights' | summary.balancing_weights |
| Summary method for objects of class 'causal_estimates' | summary.causal_estimates |