Bayesian Meta-Analysis and Network Meta-Analysis

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Documentation for package ‘metapack’ version 0.2.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

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