Bayesian Meta-Analysis and Network Meta-Analysis
Fit Bayesian Network Meta-Regression Models
Fit Bayesian Inference for Meta-Regression
bmeta_analyze supersedes the previous two functions: bayes_parobs, bay...
get the posterior mean of fixed-effect coefficients
get fitted values
get fitted values
get the highest posterior density (HPD) interval
get the highest posterior density (HPD) interval or equal-tailed credi...
get the highest posterior density (HPD) interval
metapack: a package for Bayesian meta-analysis and network meta-analys...
get compute the model comparison measures
compute the model comparison measures
compute the model comparison measures: DIC, LPML, or Pearson's residua...
helper function encoding trial sample sizes in formulas
get goodness of fit
get goodness of fit
plot the surface under the cumulative ranking curve (SUCRA)
Print results
Print results
get surface under the cumulative ranking curve (SUCRA)
get surface under the cumulative ranking curve (SUCRA)
summary method for class "bayesnmr"
summary method for class "bayesparobs"
Contains functions performing Bayesian inference for meta-analytic and network meta-analytic models through Markov chain Monte Carlo algorithm. Currently, the package implements Hui Yao, Sungduk Kim, Ming-Hui Chen, Joseph G. Ibrahim, Arvind K. Shah, and Jianxin Lin (2015) <doi:10.1080/01621459.2015.1006065> and Hao Li, Daeyoung Lim, Ming-Hui Chen, Joseph G. Ibrahim, Sungduk Kim, Arvind K. Shah, Jianxin Lin (2021) <doi:10.1002/sim.8983>. 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.