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: 1.2.1
Depends: R (≥ 3.5.0), rjags (≥ 4-6)
Imports: coda, Rdpack, grDevices, forestplot, metafor, lme4, methods
Suggests: R.rsp
Published: 2022-06-13
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>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
SystemRequirements: JAGS 4.x.y (
In views: Bayesian
CRAN checks: BayesCACE results


Reference manual: BayesCACE.pdf
Vignettes: BayesCACE paper


Package source: BayesCACE_1.2.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): BayesCACE_1.2.1.tgz, r-oldrel (arm64): BayesCACE_1.2.1.tgz, r-release (x86_64): BayesCACE_1.2.1.tgz, r-oldrel (x86_64): BayesCACE_1.2.1.tgz
Old sources: BayesCACE archive


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