| bayes_nmr | Fit Bayesian Network Meta-Regression Models | 
| bayes_parobs | Fit Bayesian Inference for Meta-Regression | 
| bmeta_analyse | bmeta_analyze supersedes the previous two functions: bayes_parobs, bayes_nmr | 
| bmeta_analyze | bmeta_analyze supersedes the previous two functions: bayes_parobs, bayes_nmr | 
| cholesterol | 26 double-blind, randomized, active, or placebo-controlled clinical trials on patients with primary hypercholesterolemia sponsored by Merck & Co., Inc., Kenilworth, NJ, USA. | 
| coef.bsynthesis | get the posterior mean of fixed-effect coefficients | 
| fitted.bayesnmr | get fitted values | 
| fitted.bayesparobs | get fitted values | 
| hpd | get the highest posterior density (HPD) interval | 
| hpd.bayesnmr | get the highest posterior density (HPD) interval | 
| hpd.bayesparobs | get the highest posterior density (HPD) interval or equal-tailed credible interval | 
| metapack | metapack: a package for Bayesian meta-analysis and network meta-analysis | 
| model_comp | compute the model comparison measures: DIC, LPML, or Pearson's residuals | 
| model_comp.bayesnmr | get compute the model comparison measures | 
| model_comp.bayesparobs | compute the model comparison measures | 
| ns | helper function encoding trial sample sizes in formulas | 
| plot.bayesnmr | get goodness of fit | 
| plot.bayesparobs | get goodness of fit | 
| plot.sucra | plot the surface under the cumulative ranking curve (SUCRA) | 
| print.bayesnmr | Print results | 
| print.bayesparobs | Print results | 
| sucra | get surface under the cumulative ranking curve (SUCRA) | 
| sucra.bayesnmr | get surface under the cumulative ranking curve (SUCRA) | 
| summary.bayesnmr | 'summary' method for class "'bayesnmr'" | 
| summary.bayesparobs | 'summary' method for class "'bayesparobs'" | 
| TNM | Triglycerides Network Meta (TNM) data |