Package: GPFDA 3.1.3

Evandro Konzen

GPFDA: Gaussian Process 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>.

Authors:Jian Qing Shi, Yafeng Cheng, Evandro Konzen

GPFDA_3.1.3.tar.gz
GPFDA_3.1.3.tar.gz(r-4.5-noble)GPFDA_3.1.3.tar.gz(r-4.4-noble)
GPFDA_3.1.3.tgz(r-4.4-emscripten)GPFDA_3.1.3.tgz(r-4.3-emscripten)
GPFDA.pdf |GPFDA.html✨
GPFDA/json (API)

# Install 'GPFDA' in R:
install.packages('GPFDA', repos = 'https://cloud.r-project.org')
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascppopenmp

3.26 score 1 stars 1 packages 268 downloads 25 exports 68 dependencies

Last updated 2 years agofrom:1d65d085fd. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 30 2025
R-4.5-linux-x86_64OKMar 30 2025
R-4.4-linux-x86_64OKMar 30 2025

Exports:calcScaleDistMatscov.linearcov.materncov.pow.excov.rat.quD2distMatdistMatLineardistMatLinearSqdistMatSqgpfrgpfrPredictgprgprPredictmat2fdmgpCovMatmgprmgprPredictnsgpCovMatnsgpCovMatAsymnsgprnsgprPredictplotImageplotmgpCovFununscaledCorr

Dependencies:ashbitopscliclustercodetoolscolorspacedeldirdeSolvedoParalleldotCall64evaluatefansifarverfdafda.uscfdsfieldsFNNforeachggplot2gluegtablehdrcdehighrinterpisobanditeratorskernlabKernSmoothknitrkskSampleslabelinglatticelifecyclelocfitmagrittrmapsMASSMatrixmclustmgcvmulticoolmunsellmvtnormnlmepcaPPpillarpkgconfigpracmaR6rainbowRColorBrewerRcppRcppArmadilloRcppEigenRCurlrlangscalesspamSuppDiststibbleutf8vctrsviridisLitewithrxfunyaml

co2 data example

Rendered fromco2.Rmdusingknitr::rmarkdownon Mar 30 2025.

Last update: 2023-09-10
Started: 2021-01-11

GPFR example

Rendered fromgpfr.Rmdusingknitr::rmarkdownon Mar 30 2025.

Last update: 2021-01-26
Started: 2021-01-11

GPR - example 1

Rendered fromgpr_ex1.Rmdusingknitr::rmarkdownon Mar 30 2025.

Last update: 2021-01-26
Started: 2021-01-11

GPR - example 2

Rendered fromgpr_ex2.Rmdusingknitr::rmarkdownon Mar 30 2025.

Last update: 2021-01-26
Started: 2021-01-11

MGPR example

Rendered frommgpr.Rmdusingknitr::rmarkdownon Mar 30 2025.

Last update: 2021-01-26
Started: 2021-01-11

NSGPR example

Rendered fromnsgpr.Rmdusingknitr::rmarkdownon Mar 30 2025.

Last update: 2021-01-26
Started: 2021-01-11

Citation

To cite package ‘GPFDA’ in publications use:

Shi JQ, Cheng Y, Konzen E (2023). GPFDA: Gaussian Process for Functional Data Analysis. R package version 3.1.3, https://CRAN.R-project.org/package=GPFDA.

ATTENTION: This citation information has been auto-generated from the package DESCRIPTION file and may need manual editing, see ‘help("citation")’.

Corresponding BibTeX entry: 

  @Manual{,
    title = {GPFDA: Gaussian Process for Functional Data Analysis},
    author = {Jian Qing Shi and Yafeng Cheng and Evandro Konzen},
    year = {2023},
    note = {R package version 3.1.3},
    url = {https://CRAN.R-project.org/package=GPFDA},
  }