Gaussian Process for Functional Data Analysis: The GPFDA Package for R

2021/01/30 by Evandro Konzen, Konzen, Evandro, Yafeng Cheng +3
Computer Science · Engineering · Mathematics · #Computation (stat.CO) #Control Systems and Identification #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #stat.CO #stat.ME

paper · pdf · doi:10.48550/arxiv.2102.00249

arxiv created 2021/01/30 · openalex publication_date 2021/01/30 · arxiv updated 2021/02/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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