bbricks: Bayesian Methods and Graphical Model Structures for Statistical Modeling

A class of frequently used Bayesian parametric and nonparametric model structures, as well as a set of tools for common analytical tasks. Structures include Gaussian and Normal-Inverse-Wishart conjugate structure, Gaussian and Normal-Inverse-Gamma conjugate structure, Categorical and Dirichlet conjugate structure, Dirichlet Process on positive integers, Dirichlet Process in general, Hierarchical Dirichlet Process ... Tasks include updating posteriors, calculating marginal likelihood, calculating posterior predictive densities, sampling from posterior predictive distributions, calculating "Maximum A Posteriori" (MAP) estimates ... See Murphy (2012, <doi:10.1080/09332480.2014.914768>), Koller and Friedman (2009, <doi:10.1017/s0269888910000275>) and Andrieu, de Freitas, Doucet and Jordan (2003, <doi:10.1023/A:1020281327116>) for more information. See <> to get started.

Version: 0.1.1
Suggests: knitr, rmarkdown
Published: 2020-03-25
Author: Haotian Chen ORCID iD [aut, cre]
Maintainer: Haotian Chen <chenhaotian.jtt at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: NEWS
CRAN checks: bbricks results


Reference manual: bbricks.pdf
Vignettes: bbricks: Getting Started
Package source: bbricks_0.1.1.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel:
OS X binaries: r-release: bbricks_0.1.1.tgz, r-oldrel: not available


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