/
package.R
44 lines (41 loc) · 1.34 KB
/
package.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
# package file
#' greta: simple and scalable statistical modelling in R
#' @name greta
#'
#' @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](https://greta-stats.org) for more information
#' including
#' [tutorials](https://greta-stats.org/articles/get_started.html) and
#' [examples](https://greta-stats.org/articles/example_models.html).
#'
#' @docType package
#' @importFrom tensorflow tf
#' @examples
#' \dontrun{
#' # 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)
#' }
"_PACKAGE"
# clear CRAN checks spotting floating global variables
#' @importFrom utils globalVariables
utils::globalVariables("N", "greta")