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
DOI:	10.32614/CRAN.package.noncomplyR
Author:	Scott Coggeshall [aut, cre]
Maintainer:	Scott Coggeshall <sscogges at uw.edu>
License:	GPL-2
NeedsCompilation:	no
CRAN checks:	noncomplyR results

Documentation:

Reference manual:	noncomplyR.pdf
Vignettes:	Introduction to noncomplyR

Downloads:

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

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