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 uw.edu>
License: GPL-2
NeedsCompilation: no
CRAN checks: noncomplyR results

Downloads:

Reference manual: noncomplyR.pdf
Vignettes: Introduction to noncomplyR
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
OS X El Capitan binaries: r-release: noncomplyR_1.0.tgz
OS X Mavericks binaries: r-oldrel: noncomplyR_1.0.tgz

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