gplite

An R package for fitting some of the most common Gaussian process (GP) models. Implements Laplace and EP approximations for handling non-Gaussian observation models, performs hyperparameter optimization using maximum marginal likelihood (or posterior), and implements some common sparse approximations for handling larger datasets. Provides also tools for model assessment and comparison via leave-one-out (LOO) cross-validation.

The syntax has taken a lot of inspiration from that of GPstuff but the intention of the package is not to be a GPstuff clone for R.

Resources

• Quickstart tutorial (notebook)
• Open an issue / ask question (GitHub issues for bug reports, questions, and feature requests)

Installation

• Install the latest release from CRAN

install.packages('gplite')

• To install the latest development version from GitHub, use the following commands (requires devtools package):

if (!require(devtools)) {
  install.packages("devtools")
  library(devtools)
}
devtools::install_github('jpiironen/gplite', build_vignettes = TRUE)

Example

library(gplite)
library(ggplot2)

# create some toy 1d regression data
set.seed(32004)
n <- 200
sigma <- 0.1
x <- rnorm(n)
y <- sin(3*x)*exp(-abs(x)) +  rnorm(n)*sigma

# set up the gp model, and optimize the hyperparameters
gp <- gp_init(cfs = cf_sexp(), lik = lik_gaussian())
gp <- gp_optim(gp, x, y)

# compute the predictive mean and variance in a grid of points
xt <- seq(-4, 4, len=300)
pred <- gp_pred(gp, xt, var=T)

# visualize
mu <- pred$mean
lb <- pred$mean - 2*sqrt(pred$var)
ub <- pred$mean + 2*sqrt(pred$var)
ggplot() +
  geom_ribbon(aes(x=xt, ymin=lb, ymax=ub), fill='lightgray') +
  geom_line(aes(x=xt, y=mu), size=1) +
  geom_point(aes(x=x, y=y), size=0.5) +
  xlab('x') + ylab('y')

Citing

If you find the software useful, I would kindly ask to use following citation:

Piironen, Juho (2021). gplite: Implementation for the Most Common Gaussian Process Models. R package.

Bibtex:

@misc{gplite,
  author = {Piironen, Juho},
  title = {gplite: Implementation for the Most Common {G}aussian Process Models},
  note = {R package},
  year = {2021},
  url = {https://github.com/jpiironen/gplite},
}

References

Rasmussen, C. E. and Williams, C. K. I. (2006). Gaussian processes for machine learning. MIT Press. Online