Bayesian Model for CACE Analysis R package BayesCACE version 1.2.3 CRAN Task View: Bayesian (https://CRAN.R-project.org/view=Bayesian) 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. Software Depends: R (>= 3.5.0), rjags (>= 4-6) Imports: coda, Rdpack, grDevices, forestplot, metafor, lme4, methods Suggests: R.rsp Jinhui Yang <james.yangjinhui@gmail.com> Comprehensive R Archive Network (CRAN) Jinhui Yang [aut, cre] (ORCID: <https://orcid.org/0000-0001-8322-1121>), Jincheng Zhou [aut] (ORCID: <https://orcid.org/0000-0003-2641-2495>), James Hodges [ctb], Haitao Chu [ctb] (ORCID: <https://orcid.org/0000-0003-0932-598X>) GPL (>= 2) 2022-10-02 application/tgz https://CRAN.R-project.org/package=BayesCACE doi:10.32614/CRAN.package.BayesCACE