Package: cfdecomp 0.4.0
cfdecomp: Counterfactual Decomposition: MC Integration of the G-Formula
Provides a set of functions for counterfactual decomposition (cfdecomp). The functions available in this package decompose differences in an outcome attributable to a mediating variable (or sets of mediating variables) between groups based on counterfactual (causal inference) theory. By using Monte Carlo (MC) integration (simulations based on empirical estimates from multivariable models) we provide added flexibility compared to existing (analytical) approaches, at the cost of computational power or time. The added flexibility means that we can decompose difference between groups in any outcome or and with any mediator (any variable type and distribution). See Sudharsanan & Bijlsma (2019) <doi:10.4054/MPIDR-WP-2019-004> for more information.
Authors:
cfdecomp_0.4.0.tar.gz
cfdecomp_0.4.0.zip(r-4.5)cfdecomp_0.4.0.zip(r-4.4)cfdecomp_0.4.0.zip(r-4.3)
cfdecomp_0.4.0.tgz(r-4.4-any)cfdecomp_0.4.0.tgz(r-4.3-any)
cfdecomp_0.4.0.tar.gz(r-4.5-noble)cfdecomp_0.4.0.tar.gz(r-4.4-noble)
cfdecomp_0.4.0.tgz(r-4.4-emscripten)cfdecomp_0.4.0.tgz(r-4.3-emscripten)
cfdecomp.pdf |cfdecomp.html✨
cfdecomp/json (API)
NEWS
| # Install 'cfdecomp' in R: |
| install.packages('cfdecomp', repos = c('https://maartenbijlsma.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/maartenbijlsma/cfdecomp/issues
- cfd.example.data - Example Data for the cfdecomp package
Last updated 3 years agofrom:b8aa06a823. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 16 2024 |
| R-4.5-win | OK | Nov 16 2024 |
| R-4.5-linux | OK | Nov 16 2024 |
| R-4.4-win | OK | Nov 16 2024 |
| R-4.4-mac | OK | Nov 16 2024 |
| R-4.3-win | OK | Nov 16 2024 |
| R-4.3-mac | OK | Nov 16 2024 |
Exports:cfd.FUNcfd.meancfd.quantilecfd.semipar.meancfd.semipar.quantilecluster.resampleconv.mean
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Example Data for the cfdecomp package | cfd.example.data |
| Flexible Function Decomposition: decompose any function that returns a vector | cfd.FUN |
| Mean Decomposition: parametric version | cfd.mean |
| Quantile Decomposition: parametric version | cfd.quantile |
| Mean Decomposition: semiparametric version | cfd.semipar.mean |
| Quantile Decomposition: semiparametric version | cfd.semipar.quantile |
| Cluster Resampling: resampling long format longitudinal or otherwise clustered data | cluster.resample |
| Running mean function | conv.mean |
