greta is an R package for writing statistical models and fitting them by MCMC and optimisation

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.

You can install the current release version of the package from CRAN:

install.packages("greta")

Or install the development version of greta from r-universe:

install.packages("greta", repos = c("https://greta-dev.r-universe.dev", "https://cloud.r-project.org"))

(Note - installing from r-universe is just like installing from CRAN, and should be faster and more convenient than installing from GitHub)

You can also install the development version of greta via GitHub:

remotes::install_github("greta-dev/greta")

Installing Python dependencies

greta uses Google's TensorFlow and tensorflow-probability Python packages under the hood. For most users there is nothing extra to install: the first time you use greta in a session, it automatically installs a compatible Python, TensorFlow, and TensorFlow Probability via uv. So library(greta) followed by your first model usually just works.

If you need a conda environment instead (for example on an offline network), or want to pin specific versions, use install_greta_deps() and then point greta at the environment with greta_set_python("conda"). By default it installs TF 2.15.0, TFP 0.23.0, and Python 3.10; choose versions via its deps argument and greta_deps_spec(). See the "Installing dependencies" vignette, ?install_greta_deps, or ?greta_set_python for more.