FSSgam: Full Subsets Multiple Regression Using GAMs
Full-subsets information-theoretic approaches are increasingly used to explore
predictive power and variable importance when a wide range of candidate predictors
are being considered. This package provides functions that can be used to construct,
fit, and compare a complete model set of possible ecological or environmental
predictors for a given response variable of interest. Models are based on
Generalized Additive Models (GAMs) and build on the 'MuMIn' package. Advantages include
the capacity to fit more predictors than there are replicates, automatic removal of
models with correlated predictors, and support for model sets that include
interactions between factors and smooth predictors, as well as smooth-by-smooth
interactions via te(). Methods are described in
Fisher et al. (2018) <doi:10.1002/ece3.4134>.
| Version: |
1.2.0 |
| Depends: |
R (≥ 4.4.0) |
| Imports: |
doSNOW, foreach, mgcv, MuMIn, nnet, parallel, stats, utils |
| Suggests: |
covr, gamm4, Matrix, testthat (≥ 3.2.0) |
| Published: |
2026-09-28 |
| DOI: |
10.32614/CRAN.package.FSSgam (may not be active yet) |
| Author: |
Rebecca Fisher [aut, cre],
Australian Institute of Marine Science [cph] |
| Maintainer: |
Rebecca Fisher <r.fisher at aims.gov.au> |
| BugReports: |
https://github.com/beckyfisher/FSSgam_package/issues |
| License: |
Apache License (== 2.0) |
| URL: |
https://github.com/beckyfisher/FSSgam_package,
https://beckyfisher.github.io/FSSgam_package/,
https://beckyfisher.github.io/FSSgam/ |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
FSSgam results |
Documentation:
Downloads:
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