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-4.5)GPFDA_3.1.3.zip(r-4.4)GPFDA_3.1.3.zip(r-4.3)
GPFDA_3.1.3.tgz(r-4.4-x86_64)GPFDA_3.1.3.tgz(r-4.4-arm64)GPFDA_3.1.3.tgz(r-4.3-x86_64)GPFDA_3.1.3.tgz(r-4.3-arm64)
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)

Peer review:

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

25 exports 1 stars 0.71 score 63 dependencies 1 dependents 35 scripts 325 downloads

Last updated 1 years agofrom:1d65d085fd. Checks:OK: 9. Indexed: yes.

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

Dependencies:ashbitopscliclustercodetoolscolorspacedeldirdeSolvedoParalleldotCall64fansifarverfdafda.uscfdsfieldsFNNforeachggplot2gluegtablehdrcdeinterpisobanditeratorskernlabKernSmoothkskSampleslabelinglatticelifecyclelocfitmagrittrmapsMASSMatrixmclustmgcvmulticoolmunsellmvtnormnlmepcaPPpillarpkgconfigpracmaR6rainbowRColorBrewerRcppRcppArmadilloRcppEigenRCurlrlangscalesspamSuppDiststibbleutf8vctrsviridisLitewithr