fungp
Scalar, functional and hybrid-Input Gaussian Process Regression
Science Score: 36.0%
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○CITATION.cff file
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○.zenodo.json file
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✓DOI references
Found 4 DOI reference(s) in README -
○Academic publication links
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1 of 6 committers (16.7%) from academic institutions -
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○Scientific vocabulary similarity
Low similarity (12.8%) to scientific vocabulary
Repository
Scalar, functional and hybrid-Input Gaussian Process Regression
Basic Info
- Host: GitHub
- Owner: djbetancourt-gh
- Language: R
- Default Branch: master
- Size: 10.3 MB
Statistics
- Stars: 5
- Watchers: 4
- Forks: 1
- Open Issues: 0
- Releases: 7
Metadata Files
README.md
Gaussian Process Models for Scalar and Functional Inputs
Description: funGp is a regression package based on Gaussian process models. It allows inputs to be either scalar, functional (represented as vectors), or a combination of both. A dimension reduction functionality is implemented in order aid keeping the model light while keeping enough information about the inputs for the model to predict well. Moreover, funGp offers a model selection feature which allows to tune different characteristics of the model such as the active scalar and functional inputs, the type of kernel function and the family of basis function used for the projection of the inputs. This is an extension of the work presented in Betancourt et al. (2020).
Main functionalities
:smallbluediamond: Creation of regression models
:smallbluediamond: Output estimation at unobserved input points
:smallbluediamond: Random sampling from a Gaussian process model
:smallbluediamond: Heuristic optimization of model structure
Note: funGp was first developed in the frame of the RISCOPE research project, funded by the French Agence Nationale de la Recherche (ANR) for the period 2017-2021 (ANR project No. 16CE04-0011, RISCOPE.fr), and certified by SAFE Cluster.
This project is licensed under the GPL-3 License.
Installation
# Install release version from CRAN
install.packages("funGp")
# Install release version from GitHub
# way 1
library(devtools)
install_github("djbetancourt-gh/funGp", dependencies = TRUE)
# way 2
library(githubinstall)
githubinstall("funGp", dependencies = TRUE)
# Install development version from GitHub
# way 1
library(devtools)
install_github("djbetancourt-gh/funGp@develop", dependencies = TRUE)
# way 2
library(githubinstall)
githubinstall("funGp@develop", dependencies = TRUE)
Manual :book:
Gaussian Process Regression for Scalar and Functional Inputs with funGp - The in-depth tour
Authors: José Betancourt :wrench: (IMT, ENAC), François Bachoc (IMT), Thierry Klein (IMT, ENAC) and Jérémy Rohmer (BRGM).
Contributors: Yves Deville (Alpestat) and Déborah Idier (BRGM).
:wrench: maintainer - fungp.rpack@gmail.com
Acknowledgments: we are grateful to Yves Deville from Alpestat for his advice on the documentation of R packages and to Juliette Garcia from ENAC for her assistance on the stabilization of the Ant Colony algorithm for structural parameter optimization.
References
Betancourt, J., Bachoc, F., Klein, T., Idier, D., Rohmer, J., and Deville, Y. (2024), "funGp: An R Package for Gaussian Process Regression with Scalar and Functional Inputs". Journal of Statistical Software, 109, 5, 1--51. [JSS]
Betancourt, J., Bachoc, F., Klein, T., Idier, D., Pedreros, R., and Rohmer, J. (2020), "Gaussian process metamodeling of functional-input code for coastal flood hazard assessment". Reliability Engineering & System Safety, 198, 106870. [RESS] - [HAL]
Betancourt, J., Bachoc, F., Klein, T., and Gamboa, F. (2020), Technical Report: "Ant Colony Based Model Selection for Functional-Input Gaussian Process Regression. Ref. D3.b (WP3.2)". RISCOPE project. [HAL]
Betancourt, J., Bachoc, F., and Klein, T. (2020), R Package Manual: "Gaussian Process Regression for Scalar and Functional Inputs with funGp - The in-depth tour". RISCOPE project. [HAL]
Owner
- Login: djbetancourt-gh
- Kind: user
- Repositories: 1
- Profile: https://github.com/djbetancourt-gh
GitHub Events
Total
Last Year
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| djbetancourt-gh | d****t@u****o | 144 |
| François Bachoc | f****c@g****m | 55 |
| yves | d****s@a****m | 28 |
| rohmerj | j****r@b****r | 23 |
| djbetancourt-gh | 5****h | 22 |
| bachoc | b****c@m****e | 3 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 11 months ago
All Time
- Total issues: 11
- Total pull requests: 20
- Average time to close issues: 3 months
- Average time to close pull requests: 2 minutes
- Total issue authors: 3
- Total pull request authors: 1
- Average comments per issue: 1.27
- Average comments per pull request: 0.0
- Merged pull requests: 19
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- djbetancourt-gh (7)
- HenrikBengtsson (3)
Pull Request Authors
- djbetancourt-gh (23)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- cran 471 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 8
- Total maintainers: 1
cran.r-project.org: funGp
Gaussian Process Models for Scalar and Functional Inputs
- Homepage: https://djbetancourt-gh.github.io/funGp/
- Documentation: http://cran.r-project.org/web/packages/funGp/funGp.pdf
- License: GPL-3
-
Latest release: 1.0.0
published about 2 years ago
Rankings
Maintainers (1)
Dependencies
- R >= 3.5.0 depends
- doFuture * imports
- doRNG * imports
- foreach * imports
- future * imports
- knitr * imports
- methods * imports
- microbenchmark * imports
- progressr * imports
- scales * imports