Package: BayesCACE 1.2.3

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.

Authors:Jinhui Yang [aut, cre], Jincheng Zhou [aut], James Hodges [ctb], Haitao Chu [ctb]

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BayesCACE.pdf |BayesCACE.html✨
BayesCACE/json (API)

# Install 'BayesCACE' in R:
install.packages('BayesCACE', repos = c('https://formidify.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:
  • epidural_c - Meta-analysis data with full compliance information
  • epidural_ic - Meta-analysis data without full compliance information

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

jagscpp

2.00 score 332 downloads 16 exports 25 dependencies

Last updated 2 years agofrom:4a9cf5282f. Checks:9 OK. Indexed: yes.

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Doc / VignettesOKMar 21 2025
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R-4.5-linuxOKMar 21 2025
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R-4.3-winOKMar 21 2025
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Exports:cace.meta.ccace.meta.iccace.studycoda.namescoda.samples.dicmodel.meta.cmodel.meta.icmodel.studyparse.varnameplt.acfplt.densityplt.forestplt.noncompplt.traceprior.metaprior.study

Dependencies:abindbackportsbootcheckmatecodadigestforestplotlatticelme4MASSmathjaxrMatrixmetadatmetaforminqanlmenloptrnumDerivpbapplyrbibutilsRcppRcppEigenRdpackreformulasrjags

BayesCACE paper

Rendered frommain.pdf.asisusingR.rsp::asison Mar 21 2025.

Last update: 2022-01-05
Started: 2022-01-05