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
This package is considered a duplicate. The official version of this package is found at:https://gpfda.r-universe.dev/GPFDA
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:
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.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://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Uses libs:
Datasets:
- dataExampleGPFR - Data simulated in the GPFR example
- dataExampleMGPR - Data simulated in the MGPR example
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:1d65d085fd. Checks:10 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 162 | ||
| linux-devel-x86_64 | OK | 171 | ||
| source / vignettes | OK | 378 | ||
| linux-release-arm64 | OK | 156 | ||
| linux-release-x86_64 | OK | 193 | ||
| windows-devel-arm64 | OK | 371 | ||
| windows-devel-x86_64 | OK | 175 | ||
| windows-release-arm64 | OK | 280 | ||
| windows-release-x86_64 | OK | 161 | ||
| wasm-release | OK | 140 |
Exports:calcScaleDistMatscov.linearcov.materncov.pow.excov.rat.quD2distMatdistMatLineardistMatLinearSqdistMatSqgpfrgpfrPredictgprgprPredictmat2fdmgpCovMatmgprmgprPredictnsgpCovMatnsgpCovMatAsymnsgprnsgprPredictplotImageplotmgpCovFununscaledCorr
Dependencies:ashaskpasscliclustercodetoolscolorspacecpp11curldeldirdeSolvedoParalleldotCall64evaluatefarverfdafda.uscfdsfieldsFNNforeachggplot2gluegmpgtablehdrcdehighrhttrinterpisobanditeratorsjsonlitekernlabKernSmoothknitrkskSampleslabelinglatticelifecyclelocfitmapsMASSMatrixmclustmgcvmimemulticoolmvtnormnlmeopensslpcaPPpracmaR6rainbowRColorBrewerRcppRcppArmadilloRcppEigenrlangS7scalesspamSuppDistssysvctrsviridisLitewithrxfunyaml
co2 data example
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
Last update: 2021-01-26
Started: 2021-01-11
GPR - example 1
Simulating data from a GP with unidimensional input | Estimation | Prediction
Last update: 2021-01-26
Started: 2021-01-11
GPR - example 2
Simulating data from a GP with 2-dimensional input | Estimation | Prediction
Last update: 2021-01-26
Started: 2021-01-11
MGPR example
Simulating data from a multiple GP with three outputs
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
Last update: 2021-01-26
Started: 2021-01-11
