| 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.bayes.parobs | get fitted values |
| fitted.bayesnmr | get fitted values |
| hpd | get the highest posterior density (HPD) interval |
| hpd.bayes.parobs | get the highest posterior density (HPD) interval or equal-tailed credible interval |
| hpd.bayesnmr | get the highest posterior density (HPD) 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.bayes.parobs | compute the model comparison measures |
| model.comp.bayesnmr | get compute the model comparison measures |
| ns | helper function encoding trial sample sizes in formulas |
| plot.bayes.parobs | get goodness of fit |
| plot.bayesnmr | get goodness of fit |
| plot.sucra | plot the surface under the cumulative ranking curve (SUCRA) |
| print.bayes.parobs | Print results |
| print.bayesnmr | Print results |
| sucra | get surface under the cumulative ranking curve (SUCRA) |
| sucra.bayesnmr | get surface under the cumulative ranking curve (SUCRA) |
| summary.bayes.parobs | 'summary' method for class "'bayes.parobs'" |
| summary.bayesnmr | Summarize results |
| TNM | Triglycerides Network Meta (TNM) data |