Gaussian Process for Functional Data Analysis
Calculate matrices for NSGP covariance function
Calculate a covariance matrix
Second derivative of the likelihood
Calculate generalised distances
GPFDA: A package for Gaussian Process Regression for Functional Data A...
Gaussian process functional regression (GPFR) model
Prediction of GPFR model
Gaussian process regression (GPR) model
Prediction of GPR model
Create an 'fd' object from a matrix
Calculate a multivariate Gaussian processes covariance matrix given a ...
Multivariate Gaussian process regression (MGPR) model
Prediction of MGPR model
Calculate a NSGP covariance matrix given a vector of hyperparameters
Calculate an asymmetric NSGP covariance matrix
Estimation of a nonseparable and/or nonstationary covariance structure...
Prediction of NSGPR model
Plot GPFR model for either training or prediction
Plot GPR model for either training or prediction
Plot predictions of GPR model
Draw an image plot for a given two-dimensional input
Plot auto- or cross-covariance function of a multivariate Gaussian pro...
Calculate an unscaled NSGP correlation matrix
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 <https://github.com/gpfda/GPFDA-dev>.