noncomplyR: Bayesian Analysis of Randomized Experiments with Non-Compliance

Functions for Bayesian analysis of data from randomized experiments with non-compliance. The functions are based on the models described in Imbens and Rubin (1997) <doi:10.1214/aos/1034276631>. Currently only two types of outcome models are supported: binary outcomes and normally distributed outcomes. Models can be fit with and without the exclusion restriction and/or the strong access monotonicity assumption. Models are fit using the data augmentation algorithm as described in Tanner and Wong (1987) <doi:10.2307/2289457>.

Version: 1.0
Imports: MCMCpack (≥ 1.4.0), stats
Suggests: knitr
Published: 2017-08-24
Author: Scott Coggeshall [aut, cre]
Maintainer: Scott Coggeshall <sscogges at>
License: GPL-2
NeedsCompilation: no
CRAN checks: noncomplyR results


Reference manual: noncomplyR.pdf
Vignettes: Introduction to noncomplyR


Package source: noncomplyR_1.0.tar.gz
Windows binaries: r-prerel:, r-release:, r-oldrel:
macOS binaries: r-prerel (arm64): noncomplyR_1.0.tgz, r-release (arm64): noncomplyR_1.0.tgz, r-oldrel (arm64): noncomplyR_1.0.tgz, r-prerel (x86_64): noncomplyR_1.0.tgz, r-release (x86_64): noncomplyR_1.0.tgz


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