Functionality for posterior inference, sensitivity and learning quantification in the Bayesian normal-normal hierarchical model used for Bayesian meta-analysis. Provides functions for heterogeneity prior adjustment with respect to tails or the latent model complexity for half-normal (HN), half-Cauchy (HC), exponential (EXP) and Lomax (LMX) priors. The functions operate on data sets which are compatible with the bayesmeta R package on CRAN.
|Author||Manuela Ott [aut, cre], Malgorzata Roos [aut],|
|Maintainer||Manuela Ott <[email protected]>|
|Package repository||View on R-Forge|
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