Package: FSSgam
Title: Full Subsets Multiple Regression Using GAMs
Version: 1.2.0
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) <doi:10.1002/ece3.4134>.
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
NeedsCompilation: no
Packaged: 2026-09-17 00:54:17 UTC; rfisher
Author: Rebecca Fisher [aut, cre],
  Australian Institute of Marine Science [cph]
Maintainer: Rebecca Fisher <r.fisher@aims.gov.au>
Repository: CRAN
Date/Publication: 2026-09-28 07:30:08 UTC
