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GPFDA (version 3.1.3)

Gaussian Process for Functional Data Analysis

Description

Functionalities for modelling functional data with multidimensional inputs, multivariate functional data, and non-separable and/or non-stationary covariance structure of function-valued processes. In addition, there are functionalities for functional regression models where the mean function depends on scalar and/or functional covariates and the covariance structure depends on functional covariates. The development version of the package can be found on .

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Version

Install

install.packages('GPFDA')

Monthly Downloads

271

Version

3.1.3

License

GPL-3

Maintainer

Evandro Konzen

Last Published

September 10th, 2023

Functions in GPFDA (3.1.3)

Calculate an asymmetric NSGP covariance matrix

Prediction of GPR model

Prediction of NSGPR model

Create an 'fd' object from a matrix

Plot GPFR model for either training or prediction

Plot auto- or cross-covariance function of a multivariate Gaussian process

Prediction of MGPR model

Calculate a NSGP covariance matrix given a vector of hyperparameters

Draw an image plot for a given two-dimensional input

Calculate an unscaled NSGP correlation matrix

Estimation of a nonseparable and/or nonstationary covariance structure (NSGPR model)

Plot GPR model for either training or prediction

Calculate a multivariate Gaussian processes covariance matrix given a vector of hyperparameters

Multivariate Gaussian process regression (MGPR) model

Plot predictions of GPR model

Second derivative of the likelihood

Gaussian process regression (GPR) model

Calculate generalised distances

GPFDA: A package for Gaussian Process Regression for Functional Data Analysis

Prediction of GPFR model

calcScaleDistMats

Calculate matrices for NSGP covariance function

Calculate a covariance matrix

dataExampleMGPR

Data simulated in the MGPR example

dataExampleGPFR

Data simulated in the GPFR example

Gaussian process functional regression (GPFR) model