LearnBayes: Functions for Learning Bayesian Inference , LearnBayes contains a collection of functions helpful in learning the basic tenets of Bayesian statistical inference. It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions. It contains MCMC algorithms for summarizing posterior distributions defined by the user. It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling. (Source: http://cran.r-project.org/web/packages)
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Rosner, Gary L.; Laud, Purushottam W.; Johnson, Wesley O.: Bayesian thinking in biostatistics (2021)
- Wang, Min; Sun, Xiaoqian; Park, Chanseok: Bayesian analysis of Birnbaum-Saunders distribution via the generalized ratio-of-uniforms method (2016)
- Hooker, Giles; Vidyashankar, Anand N.: Bayesian model robustness via disparities (2014)
- Albert, Jim: Bayesian computation with R (2009)