| greta {greta} | R Documentation |
greta: simple and scalable statistical modelling in R
Description
greta lets you write statistical models interactively in native R code, then sample from them efficiently using Hamiltonian Monte Carlo.
The computational heavy lifting is done by TensorFlow, Google's automatic differentiation library. So greta is particularly fast where the model contains lots of linear algebra, and greta models can be run across CPU clusters or on GPUs.
See the simple example below, and take a look at the greta website for more information including tutorials and examples.
Author(s)
Maintainer: Nicholas Tierney nicholas.tierney@gmail.com (ORCID)
Authors:
Nick Golding nick.golding.research@gmail.com (ORCID)
Other contributors:
Simon Dirmeier [contributor]
Adam Fleischhacker [contributor]
Shirin Glander [contributor]
Martin Ingram [contributor]
Lee Hazel [contributor]
Lionel Hertzog [contributor]
Tiphaine Martin [contributor]
Matt Mulvahill [contributor]
Michael Quinn [contributor]
David Smith [contributor]
Paul Teetor [contributor]
Jian Yen [contributor]
See Also
Useful links:
Report bugs at https://github.com/greta-dev/greta/issues
Examples
## Not run:
# a simple Bayesian regression model for the iris data
# priors
int <- normal(0, 5)
coef <- normal(0, 3)
sd <- lognormal(0, 3)
# likelihood
mean <- int + coef * iris$Petal.Length
distribution(iris$Sepal.Length) <- normal(mean, sd)
# build and sample
m <- model(int, coef, sd)
draws <- mcmc(m, n_samples = 100)
## End(Not run)