BayesCACE: Bayesian Model for CACE Analysis

Performs CACE (Complier Average Causal Effect analysis) on either a single study or meta-analysis of datasets with binary outcomes, using either complete or incomplete noncompliance information. Our package implements the Bayesian methods proposed in Zhou et al. (2019) <doi:10.1111/biom.13028>, which introduces a Bayesian hierarchical model for estimating CACE in meta-analysis of clinical trials with noncompliance, and Zhou et al. (2021) <doi:10.1080/01621459.2021.1900859>, with an application example on Epidural Analgesia.

Version: 1.2
Depends: R (≥ 3.5.0), rjags (≥ 4-6)
Imports: coda, Rdpack, grDevices, forestplot, metafor, lme4
Suggests: R.rsp
Published: 2022-01-06
Author: Jinhui Yang ORCID iD [aut, cre], Jincheng Zhou ORCID iD [aut], James Hodges [ctb], Haitao Chu ORCID iD [ctb]
Maintainer: Jinhui Yang <james.yangjinhui at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
SystemRequirements: JAGS 4.x.y (http://mcmc-jags.sourceforge.net)
In views: Bayesian
CRAN checks: BayesCACE results

Documentation:

Reference manual: BayesCACE.pdf
Vignettes: BayesCACE paper

Downloads:

Package source: BayesCACE_1.2.tar.gz
Windows binaries: r-devel: BayesCACE_1.2.zip, r-release: BayesCACE_1.2.zip, r-oldrel: BayesCACE_1.2.zip
macOS binaries: r-release (arm64): BayesCACE_1.2.tgz, r-oldrel (arm64): BayesCACE_1.2.tgz, r-release (x86_64): BayesCACE_1.2.tgz, r-oldrel (x86_64): BayesCACE_1.2.tgz
Old sources: BayesCACE archive

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