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clusteredinterference: Causal Effects from Observational Studies with Clustered
Interference
Estimating causal effects from observational studies assuming
clustered (or partial) interference. These inverse probability-weighted
estimators target new estimands arising from population-level treatment
policies. The estimands and estimators are introduced in Barkley et al.
(2017) <doi:10.48550/arXiv.1711.04834>.
| Version: |
1.0.1 |
| Depends: |
R (≥ 3.2) |
| Imports: |
Formula (≥ 1.1-2), cubature (≥ 1.1-2), lme4 (≥ 1.1-10), numDeriv (≥ 2014.2-1), rootSolve (≥ 1.6.6) |
| Suggests: |
testthat, rprojroot, knitr, rmarkdown, covr |
| Published: |
2019-03-18 |
| Author: |
Brian G. Barkley 
[aut, cre],
Bradley Saul [ctb] |
| Maintainer: |
Brian G. Barkley <BarkleyBG at outlook.com> |
| BugReports: |
http://github.com/BarkleyBG/clusteredinterference/issues |
| License: |
GPL-3 |
| URL: |
http://github.com/BarkleyBG/clusteredinterference |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| In views: |
CausalInference |
| CRAN checks: |
clusteredinterference results |
Documentation:
Downloads:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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