Reference Analysis for Bayesian Meta-Analysis
Calibration of the Hellinger distance
Density function of the square-root generalized conventional (SGC) ben...
Density function of the square-root inverse generalized conventional (...
Model fitting for reference analysis using 2 benchmarks: Posterior inf...
Model fitting for reference analysis using 5 benchmarks: Posterior inf...
Hellinger distance between two probability densities
Hellinger distance between marignal posterior densities of two bayesme...
Approximate moment-based Hellinger distance computation between two pr...
Optimization function for the SGC(m) prior: Adjust the prior to a targ...
Optimization function for the SIGC(M) prior: Adjust the prior to a tar...
Optimization function for the SGC(m) prior: Approximate Jeffreys refer...
Optimization function for the SIGC(m) prior: Approximate Jeffreys refe...
Reference analysis plot based on a data frame using 2 benchmarks: Plot...
Reference analysis plot based on a data frame using 5 benchmarks: Plot...
Reference analysis plot based on bayesmeta fits: Plot heterogeneity be...
Normal posterior for the overall mean parameter in the fixed effects m...
Posterior reference analysis based on a data frame using 2 benchmarks
Posterior reference analysis based on a data frame using 3 benchmarks
Posterior reference analysis based on bayesmeta fits
Prior reference analysis based on a data frame using 5 benchmarks
Prior reference analysis based on bayesmeta fits
tools:::Rd_package_title("ra4bayesmeta")
Reference standard deviation
Functionality for performing a principled reference analysis in the Bayesian normal-normal hierarchical model used for Bayesian meta-analysis, as described in Ott, Plummer and Roos (2021) <doi:10.1002/sim.9076>. Computes a reference posterior, induced by a minimally informative improper reference prior for the between-study (heterogeneity) standard deviation. Determines additional proper anti-conservative (and conservative) prior benchmarks. Includes functions for reference analyses at both the posterior and the prior level, which, given the data, quantify the informativeness of a heterogeneity prior of interest relative to the minimally informative reference prior and the proper prior benchmarks. The functions operate on data sets which are compatible with the 'bayesmeta' package.