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sl4bayesmeta: Sensitivity and learning for Bayesian meta-analysis
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.
Getting started
Browse package contents
Package details |
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| Author | Manuela Ott [aut, cre], Malgorzata Roos [aut], |
| Maintainer | Manuela Ott <manuela.ott@uzh.ch> |
| License | GPL (>=2) |
| Version | 0.3-1 |
| Package repository | View on R-Forge |
| Installation |
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