Package: GPFDA 3.1.3

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.zip(r-4.7-x86_64)GPFDA_3.1.3.zip(r-4.7-arm64)GPFDA_3.1.3.zip(r-4.6-x86_64)GPFDA_3.1.3.zip(r-4.6-arm64)GPFDA_3.1.3.zip(r-4.5-x86_64)
GPFDA_3.1.3.tgz(r-4.6-x86_64)GPFDA_3.1.3.tgz(r-4.6-arm64)GPFDA_3.1.3.tgz(r-4.5-x86_64)GPFDA_3.1.3.tgz(r-4.5-arm64)
GPFDA_3.1.3.tar.gz(r-4.7-arm64)GPFDA_3.1.3.tar.gz(r-4.7-x86_64)GPFDA_3.1.3.tar.gz(r-4.6-arm64)GPFDA_3.1.3.tar.gz(r-4.6-x86_64)
GPFDA_3.1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
GPFDA/json (API)

# Install 'GPFDA' in R:
install.packages('GPFDA', repos = c('https://gpfda.r-universe.dev', '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

3.96 score 1 stars 1 packages 51 scripts 451 downloads 25 exports 63 dependencies

Last updated from:1d65d085fd. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK180
linux-devel-x86_64OK208
source / vignettesOK376
linux-release-arm64OK176
linux-release-x86_64OK211
macos-release-arm64OK252
macos-release-x86_64OK378
macos-oldrel-arm64OK129
macos-oldrel-x86_64OK350
windows-devel-arm64OK397
windows-devel-x86_64OK230
windows-release-arm64OK396
windows-release-x86_64OK208
windows-oldrel-x86_64OK182
wasm-releaseOK125

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

Dependencies:ashbitopscliclustercodetoolscolorspacecpp11deldirdeSolvedoParalleldotCall64evaluatefarverfdafda.uscfdsfieldsFNNforeachggplot2gluegtablehdrcdehighrinterpisobanditeratorskernlabKernSmoothknitrkskSampleslabelinglatticelifecyclelocfitmapsMASSMatrixmclustmgcvmulticoolmvtnormnlmepcaPPpracmaR6rainbowRColorBrewerRcppRcppArmadilloRcppEigenRCurlrlangS7scalesspamSuppDistsvctrsviridisLitewithrxfunyaml

co2 data example
Loading GPFDA package | Loading co2 data | Using exponential covariance kernel | Using a customised covariance kernel | Defining a customised covariance kernel | Defining the first derivatives | Defining the second derivatives | Defining a diagonal matrix including the signal variance | Fitting the GPR model with the customised kernel and making predictions
Rendered fromco2.Rmdusingknitr::rmarkdown

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

GPFR example
Plot of raw data | FR fit for training data | GPFR fit for training data | Type I prediction: $y_{M+1}$ observed | Type I prediction: $y_{M+1}$ partially observed | Type II prediction: $y_{M+1}$ not observed
Rendered fromgpfr.Rmdusingknitr::rmarkdown

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

GPR - example 1
Simulating data from a GP with unidimensional input | Estimation | Prediction
Rendered fromgpr_ex1.Rmdusingknitr::rmarkdown

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

GPR - example 2
Simulating data from a GP with 2-dimensional input | Estimation | Prediction
Rendered fromgpr_ex2.Rmdusingknitr::rmarkdown

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

MGPR example
Simulating data from a multiple GP with three outputs
Rendered frommgpr.Rmdusingknitr::rmarkdown

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

NSGPR example
Simulating data from a GP with nonstationary and nonseparable covariance structure | Estimating NSGP covariance function | Prediction | Plots of covariance functions
Rendered fromnsgpr.Rmdusingknitr::rmarkdown

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