Package: BayesCACE Type: Package Title: Bayesian Model for CACE Analysis Version: 1.2.3 Date: 2022-10-1 Authors@R: c(person(given = "Jinhui", family = "Yang", role = c("aut", "cre"), email = "james.yangjinhui@gmail.com", comment = c(ORCID = "0000-0001-8322-1121")), person(given = "Jincheng", family = "Zhou", role = "aut", comment = c(ORCID = "0000-0003-2641-2495")), person(given = "James", family = "Hodges", role = "ctb"), person(given = "Haitao", family = "Chu", role = "ctb", comment = c(ORCID = "0000-0003-0932-598X")) ) Depends: R (>= 3.5.0), rjags (>= 4-6) Imports: coda, Rdpack, grDevices, forestplot, metafor, lme4, methods SystemRequirements: JAGS 4.x.y (http://mcmc-jags.sourceforge.net) Description: 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) , which introduces a Bayesian hierarchical model for estimating CACE in meta-analysis of clinical trials with noncompliance, and Zhou et al. (2021) , with an application example on Epidural Analgesia. License: GPL (>= 2) NeedsCompilation: no RoxygenNote: 7.1.2 RdMacros: Rdpack Encoding: UTF-8 LazyData: true Suggests: R.rsp VignetteBuilder: R.rsp Packaged: 2026-07-08 06:55:55 UTC; root Author: Jinhui Yang [aut, cre] (), Jincheng Zhou [aut] (), James Hodges [ctb], Haitao Chu [ctb] () Maintainer: Jinhui Yang Config/pak/sysreqs: cmake make jags Repository: https://formidify.r-universe.dev Date/Publication: 2022-10-02 14:00:02 UTC RemoteUrl: https://github.com/cran/BayesCACE RemoteRef: HEAD RemoteSha: 4a9cf5282f0ffc9387177843bb69e5d6c1171cf9