greta: Simple and Scalable Statistical Modelling in R

Write statistical models in R and fit them by MCMC and optimisation on CPUs and GPUs, using Google 'TensorFlow'. greta lets you write your own model like in BUGS, JAGS and Stan, except that you write models right in R, it scales well to massive datasets, and it’s easy to extend and build on. See the website for more information, including tutorials, examples, package documentation, and the greta forum.

Version: 0.3.1
Depends: R (≥ 3.0)
Imports: R6, tensorflow (≥ 1.13.0), reticulate, progress (≥ 1.2.0), future, coda, methods
Suggests: knitr, rmarkdown, DiagrammeR, bayesplot, lattice, testthat, mvtnorm, MCMCpack, rmutil, extraDistr, truncdist, tidyverse, fields, MASS, abind, spelling
Published: 2019-08-09
Author: Nick Golding ORCID iD [aut, cre], Simon Dirmeier [ctb], Adam Fleischhacker [ctb], Shirin Glander [ctb], Martin Ingram [ctb], Lee Hazel [ctb], Tiphaine Martin [ctb], Matt Mulvahill [ctb], Michael Quinn [ctb], David Smith [ctb], Paul Teetor [ctb], Jian Yen [ctb]
Maintainer: Nick Golding <nick.golding.research at>
License: Apache License 2.0
NeedsCompilation: no
SystemRequirements: Python (>= 2.7.0) with header files and shared library; TensorFlow (v1.14;; TensorFlow Probability (v0.7.0;
Language: en-GB
Materials: NEWS
CRAN checks: greta results


Reference manual: greta.pdf
Vignettes: Example models
Get started with greta
Package source: greta_0.3.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: greta_0.3.1.tgz, r-oldrel: greta_0.3.1.tgz
Old sources: greta archive

Reverse dependencies:

Reverse imports: causact


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