GPFDA: Gaussian Process Regression for Functional Data Analysis

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>.

Version: 3.0.0
Depends: R (≥ 3.6)
Imports: Rcpp (≥ 1.0.2), splines, mgcv, MASS, mvtnorm, fields, interp, stats, graphics, grDevices, fda, fda.usc
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown
Published: 2021-01-11
Author: Jian Qing Shi, Yafeng Cheng, Evandro Konzen
Maintainer: Evandro Konzen <gpfda.r at gmail.com>
License: GPL-3
NeedsCompilation: yes
In views: FunctionalData
CRAN checks: GPFDA results

Downloads:

Reference manual: GPFDA.pdf
Vignettes: co2
gpfr
gpr_ex1
gpr_ex2
mgpr
nsgpr
Package source: GPFDA_3.0.0.tar.gz
Windows binaries: r-devel: GPFDA_3.0.0.zip, r-release: GPFDA_3.0.0.zip, r-oldrel: GPFDA_3.0.0.zip
macOS binaries: r-release: GPFDA_3.0.0.tgz, r-oldrel: GPFDA_3.0.0.tgz
Old sources: GPFDA archive

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