Gaussian Process Models for Scalar and Functional Inputs
Overview
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). An in-depth tutorial in the use of funGp is provided in Betancourt et al. (2024) doi:10.18637/jss.v109.i05 and Metamodeling background is provided in Betancourt et al. (2020) doi:10.1016/j.ress.2020.106870. The algorithm for structural parameter optimization is described in https://hal.science/hal-02532713.
Main package life cycle updates
- This release accompanies the publication of the funGp in-depth manual in the Journal of Statistical Software.
Minor documentation updates
- Maintainer email updated from
djbetancourt@uninorte.edu.cotofungp.rpack@gmail.comto ensure a permanet and stable channel of communication with the community. - A couple syntax fixes in the package notation to keep up with recent updates in Roxygen2.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 30 (3 internal)includes a package overview page
- Examples that run
- 94%17 run in a plain check · 1 never run
- Exported functions with examples
- 64%
- Documented parameters
- 100%
- Return-value docs
- 91%
- Help pages citing references
- 17%
Downloads
Repository
- PRs merged (all time)
- 95%
- Median days to close, 50 most recently updated closed issues
- 0
- Median days to close, 50 most recently updated closed PRs
- 0
Stars over time
Forks over time
Releases over time
Issues over time
6 development-tooling and community-health practices detected across 4 families in the upstream repository
Checks run against github.com/djbetancourt-gh/fungp on 2026-10-21.
Dependencies
Installing funGp adds 28 packages (9 with compiled code, 7.6 MB of source tarballs). Heaviest: farver 1.2 MB, viridisLite 1.2 MB, rlang 766 KB.
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- 1.0.0Latest
- RR 4.4.0 released · 2024-04-24
- 0.3.22023-04-25 · diff ↗
- RR 4.3.0 released · 2023-04-21
- 0.3.12023-01-22 · diff ↗
- 0.3.02022-05-30 · diff ↗ ·
removed 7 exports
format4pred, plotEvol, plotLAA, plotLOO, plotPreds, plotSims, plotXadded 1 export
modelDef - RR 4.2.0 released · 2022-04-22
- unarchivedReturned to CRAN2021-07-22
- 0.2.2
- RR 4.1.0 released · 2021-05-18
- archivedRemoved from CRAN2021-04-29check problems were not corrected in time
- 0.2.12020-11-24 · diff ↗
- 0.2.0
- RR 4.0.0 released · 2020-04-24
Show 2 earlier eventsHide 2 earlier events
- 0.1.02020-04-22
- RR 3.6.0 released · 2019-04-26
Package metadata
- First published
- 2020-04-22
- Total releases
- 8 / 6 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 89 KB / 1 file
- Download size
- 263 KB
Cite
Cite this package
Run in R for the authors' entry:
citation("funGp")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.18637/jss.v109.i05.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.