  We present and describe the GPFDA package for R. The package provides
flexible functionalities for dealing with Gaussian process regression (GPR)
models for functional data. Multivariate functional data, functional data with
multidimensional inputs, and nonseparable and/or nonstationary covariance
structures can be modeled. In addition, the package fits functional regression
models where the mean function depends on scalar and/or functional covariates
and the covariance structure is modeled by a GPR model. In this paper, we
present the versatility of GPFDA with respect to mean function and covariance
function specifications and illustrate the implementation of estimation and
prediction of some models through reproducible numerical examples.
