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metapack (version 0.1.x) provides function(s) to perform Bayesian inference for (network) meta-analytic models including the ones introduced in the following papers: + Yao, H., Kim, S., Chen, M. H., Ibrahim, J. G., Shah, A. K., & Lin, J. (2015). Bayesian inference for multivariate meta-regression with a partially observed within-study sample covariance matrix. Journal of the American Statistical Association, 110(510), 528-544. + Li, H, Lim, D, Chen, M-H, et al. Bayesian network meta‐regression hierarchical models using heavy-tailed multivariate random effects with covariate-dependent variances. Statistics in Medicine. 2021; 1-22. doi:10.1002/sim.8983

metapack takes advantage of formula-parsing to extract relevant information to configure a meta-analytic model. Aside from the data characteristic (aggregate v. IPD) and the response type (univariate v. multivariate), all other modeling choices fall into prior specification.

To see the model specification, please refer to the corresponding papers or the long-form vignette of this package.



Getting help

If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub. For questions and other discussion, please email the maintainer.