Contains functions performing Bayesian inference for meta-analytic and network meta-analytic models through Markov chain Monte Carlo algorithm. Currently, the package implements Yao, Kim, Chen, Ibrahim, Shah, and Jianxin Lin (2015) <doi:10.1080/01621459.2015.1006065> and network meta-regression models using heavy-tailed multivariate random effects with covariate-dependent variances. For maximal computational efficiency, the Markov chain Monte Carlo samplers for each model, written in C++, are fine-tuned. This software has been developed under the auspices of the National Institutes of Health and Merck & Co., Inc., Kenilworth, NJ, USA.
| Version: | 0.1.1 |
| Depends: | R (≥ 3.4) |
| Imports: | Rcpp, ggplot2, methods, gridExtra |
| LinkingTo: | Rcpp, RcppArmadillo, RcppProgress, BH |
| Suggests: | knitr, rmarkdown |
| Published: | 2021-02-23 |
| Author: | Daeyoung Lim [aut, cre], Ming-Hui Chen [ctb], Sungduk Kim [ctb], Joseph Ibrahim [ctb], Arvind Shah [ctb], Jianxin Lin [ctb] |
| Maintainer: | Daeyoung Lim <daeyoung.lim at uconn.edu> |
| BugReports: | https://github.com/daeyounglim/metapack/issues |
| License: | GPL (≥ 3) |
| URL: | http://merlot.stat.uconn.edu/packages/metapack/ |
| NeedsCompilation: | yes |
| Citation: | metapack citation info |
| Materials: | README NEWS |
| CRAN checks: | metapack results |
| Reference manual: | metapack.pdf |
| Vignettes: |
Introduction to metapack |
| Package source: | metapack_0.1.1.tar.gz |
| Windows binaries: | r-devel: metapack_0.1.1.zip, r-release: metapack_0.1.1.zip, r-oldrel: metapack_0.1.1.zip |
| macOS binaries: | r-release: metapack_0.1.1.tgz, r-oldrel: metapack_0.1.0.tgz |
| Old sources: | metapack archive |
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