Package: FSSgam Title: Full Subsets Multiple Regression Using GAMs Version: 1.2.0.9000 Authors@R: c( person("Rebecca", "Fisher", email = "r.fisher@aims.gov.au", role = c("aut", "cre")), person("Australian Institute of Marine Science", role = "cph") ) Description: 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) . Depends: R (>= 4.4.0) License: Apache License (== 2.0) Encoding: UTF-8 LazyData: true Imports: doSNOW, foreach, mgcv, MuMIn, nnet, parallel, stats, utils URL: https://github.com/beckyfisher/FSSgam_package, https://beckyfisher.github.io/FSSgam_package/, https://beckyfisher.github.io/FSSgam/ BugReports: https://github.com/beckyfisher/FSSgam_package/issues Suggests: covr, gamm4, Matrix, testthat (>= 3.2.0) Config/testthat/edition: 3 Config/roxygen2/version: 8.0.0 Repository: https://beckyfisher.r-universe.dev Date/Publication: 2026-09-29 01:55:43 UTC RemoteUrl: https://github.com/beckyfisher/fssgam_package RemoteRef: HEAD RemoteSha: d32db19303bdbf12321635d9e6d4ada8704e1634 NeedsCompilation: no Packaged: 2026-09-29 02:15:03 UTC; root Author: Rebecca Fisher [aut, cre], Australian Institute of Marine Science [cph] Maintainer: Rebecca Fisher