  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).
