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funGp  

Gaussian Process Models for Scalar and Functional Inputs
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]
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 29 Apr. 2025.
Previous versions and publish date:
0.1.0 (2020-04-22 19:24), 0.2.0 (2020-11-17 10:10), 0.2.1 (2020-11-24 06:30), 0.2.2 (2021-07-22 10:40), 0.3.0 (2022-05-30 13:20), 0.3.1 (2023-01-22 01:40), 0.3.2 (2023-04-25 09:40)
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
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