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funGp
View on CRAN: Click
here
Download and install funGp package within the R console
Install from CRAN:
install.packages("funGp")
Install from Github:
library("remotes")
install_github("cran/funGp")
Install by package version:
library("remotes")
install_version("funGp", "1.0.0")
Attach the package and use:
library("funGp")
Maintained by
Jose Betancourt
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.funGp . https://github.com/djbetancourt-gh/funGp/issues . https://djbetancourt-gh.github.io/funGp/ . https://github.com/djbetancourt-gh/funGp . funGp citation info . funGp results . funGp.pdf . funGp_1.0.0.tar.gz . funGp_1.0.0.zip . funGp_1.0.0.zip . funGp_1.0.0.zip . funGp_1.0.0.tgz . funGp_1.0.0.tgz . funGp_1.0.0.tgz . funGp_1.0.0.tgz . funGp_1.0.0.tgz . funGp_1.0.0.tgz . funGp archive . https://CRAN.R-project.org/package=funGp .
First Published: 2020-04-22
Latest Update: 2023-04-25
Description:
Construction and smart selection of Gaussian process models
for analysis of computer experiments
with emphasis on treatment of functional inputs that are regularly sampled. This package
offers: (i) flexible modeling of functional-input regression
problems through the fairly general Gaussian process model; (ii)
built-in dimension reduction for functional inputs; (iii)
heuristic optimization of the structural parameters of the model
(e.g., active inputs, kernel function, type of distance).
Metamodeling background is provided in
Betancourt et al. (2020) .
The algorithm for structural parameter optimization is described
in .
How to cite:
Jose Betancourt (2020). funGp: Gaussian Process Models for Scalar and Functional Inputs. R package version 1.0.0, https://cran.r-project.org/web/packages/funGp. Accessed 07 Apr. 2025.
Previous versions and publish date:
Other packages that cited funGp R package
View funGp citation profile
Other R packages that funGp depends,
imports, suggests or enhances
Complete documentation for funGp
Functions, R codes and Examples using
the funGp R package
Some associated functions: Xfgpm-class . antsLog-class . black-boxes . decay . decay2probs . factoryCall-class . fgpm-class . fgpm . fgpm_factory . funGp-package . gaussian_cor . get_active_in . kernel-class . matern32_cor . matern52_cor . modelCall-class . modelDef . plot-Xfgpm-method . plot-fgpm-method . plot.predict.fgpm . plot.simulate.fgpm . precalculated_Xfgpm_objects . predict-methods . proj-Class . simulate-methods . sub-sub-Xfgpm-method . summary-Xfgpm-method . summary-fgpm-method . update-methods . which_on .
Some associated R codes: 0_funGp_Doc.R . 0_show_Doc.R . 0_summary_Doc.R . 1_Xfgpm_Class.R . 1_fgpm_Class.R . 2_fgpKern_Class.R . 2_fgpProj_Class.R . 3_ant_admin.R . 3_ant_search.R . 3_training_F.R . 3_training_S.R . 3_training_SF.R . 4_prediction_F.R . 4_prediction_S.R . 4_prediction_SF.R . 5_simulation_F.R . 5_simulation_S.R . 5_simulation_SF.R . 6_updating.R . 7_blackBoxFunctions.R . 7_checkingFunctions.R . 7_correlFunctions.R . 7_dimRedFunctions.R . 7_distanceFunctions.R . 7_plottingFunctions.R . 7_plottingFunctionsStandard.R . 8_outilsCode.R . 8_outilsStats.R . 8_precalculated_Xfgpm_objects.R . Full funGp package functions and examples
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