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) <arXiv:1711.04834>.

Version: 1.0.0
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: 2018-02-13
Author: Brian G. Barkley ORCID iD [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: README NEWS
CRAN checks: clusteredinterference results

Downloads:

Reference manual: clusteredinterference.pdf
Vignettes: estimate-policyFX
Package source: clusteredinterference_1.0.0.tar.gz
Windows binaries: r-devel: clusteredinterference_1.0.0.zip, r-release: clusteredinterference_1.0.0.zip, r-oldrel: clusteredinterference_1.0.0.zip
OS X binaries: r-release: clusteredinterference_1.0.0.tgz, r-oldrel: clusteredinterference_1.0.0.tgz

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