Robert B. Gramacy Professor of Statistics

Particle learning of Gaussian processes

plgp is an R package implementing sequential Monte Carlo inference for fully Bayesian Gaussian process (GP) models by particle learning (PL). The sequential nature of inference and the active learning (AL) hooks provided facilitate thrifty sequential design and optimization.

This software is licensed under the GNU Lesser Public License (LGPL), version 2 or later. See the change log and an archive of previous versions.

The current version provides:

  • a generic PL interface
  • static classification and regression GP models with three types of correlation functions: isotropic, separable and single-index Gaussian
  • sequential design for optimization of (noisy) real-valued functions by expected improvement (EI) statistic using a regression GP model
  • sequential design for exploring classification boundaries by the predictive entropy statistic via a classification GP model
  • sequential design for optimization under known and unknown constraints by an integrated expected conditional improvement (IECI) statistic using a hybrid regression-classification GP model

Obtaining the package

  • Download R from by selecting the version for your operating system.
  • Install the plgp, mvtnorm and tgp packages, from within R.
    R> install.packages(c("plgp", "mvtnorm", "tgp"))
  • Optionally, install the akima, ellipse and splancs packages.
    R> install.packages(c("akima", "ellipse", "splancs"))
  • Load the library as you would for any R library.
    R> library(plgp)


  • See the package documentation. A pdf version of the reference manual, or help pages, is also available. The help pages can be accessed from within R.
  • The best way to acquaint yourself with the functionality of this package is to run the demos which illustrate the examples contained in the papers referenced below. Try starting with:
    R> help(package=plgp)
    R> ?plgp    # follow the examples which point to the demos
    R> demo(package="plgp")   # for a direct listing of the demos