FastGP: An R Package for Gaussian Processes
Abstract
Despite their promise and ubiquity, Gaussian processes (GPs) can be difficult to use in practice due to the computational impediments of fitting and sampling from them. Here we discuss a short R package for efficient multivariate normal functions which uses the Rcpp and RcppEigen packages at its core. GPs have properties that allow standard functions to be sped up; as an example we include functionality for Toeplitz matrices whose inverse can be computed in O(n^2) time with methods due to Trench and Durbin (Golub & Van Loan 1996), which is particularly apt when time points (or spatial locations) of a Gaussian process are evenly spaced, since the associated covariance matrix is Toeplitz in this case. Additionally, we include functionality to sample from a latent variable Gaussian process model with elliptical slice sampling (Murray, Adams, & MacKay 2010).
Cite
@article{arxiv.1507.06055,
title = {FastGP: An R Package for Gaussian Processes},
author = {Giri Gopalan and Luke Bornn},
journal= {arXiv preprint arXiv:1507.06055},
year = {2015}
}
Comments
Paper submitted to JSS and R package submitted to CRAN. Temporarily available at: https://github.com/ggopalan/FastGP