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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "FSSgam" in publications use:'
type: software
license: Apache-2.0
title: 'FSSgam: Full Subsets Multiple Regression Using GAMs'
version: 1.2.0.9000
doi: 10.32614/CRAN.package.FSSgam
identifiers:
- type: url
  value: https://beckyfisher.github.io/FSSgam/
abstract: 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) <https://doi.org/10.1002/ece3.4134>.
authors:
- family-names: Fisher
  given-names: Rebecca
  email: r.fisher@aims.gov.au
repository: https://beckyfisher.r-universe.dev
repository-code: https://github.com/beckyfisher/FSSgam_package
commit: d32db19303bdbf12321635d9e6d4ada8704e1634
url: https://beckyfisher.github.io/FSSgam_package/
date-released: '2026-09-29'
contact:
- family-names: Fisher
  given-names: Rebecca
  email: r.fisher@aims.gov.au