Full Subsets Multiple Regression Using GAMs R package FSSgam version 1.2.0 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>. Software Depends: R (>= 4.4.0) Imports: doSNOW, foreach, mgcv, MuMIn, nnet, parallel, stats, utils Suggests: covr, gamm4, Matrix, testthat (>= 3.2.0) Rebecca Fisher <r.fisher@aims.gov.au> Comprehensive R Archive Network (CRAN) Rebecca Fisher [aut, cre], Australian Institute of Marine Science [cph] Apache License (== 2.0) 2026-09-28 application/tgz https://CRAN.R-project.org/package=FSSgam doi:10.32614/CRAN.package.FSSgam