clusteredinterference (1.0.0)

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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) .

Maintainer: Brian G. Barkley
Author(s): Brian G. Barkley [aut, cre] (<>), Bradley Saul [ctb]

License: GPL-3

Uses: cubature, Formula, lme4, numDeriv, rootSolve, testthat, knitr, rmarkdown, covr, rprojroot

Released 9 months ago.



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