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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 "glmfsa" in publications use:'
type: software
license: GPL-3.0-only
title: 'glmfsa: Feature Selection with Annealing for Sparse Generalized Linear Models'
version: 1.0.1
abstract: Fits sparse generalized linear models under an exact cardinality constraint
  using Feature Selection with Annealing (FSA), the method of Barbu, She, Ding and
  Gramajo (2017) <https://doi.org/10.1109/TPAMI.2016.2544315>. Rather than inducing
  sparsity through an L1 (lasso-type) penalty, FSA alternates a gradient step on the
  loss with a step that removes the lowest-magnitude coefficients according to an
  annealing schedule, so the user specifies the number of variables directly and the
  surviving coefficients carry no shrinkage bias. The working design matrix physically
  shrinks as iterations proceed, which makes the cost of a fit fall over time and
  the method well suited to problems with very many features. Gaussian, binomial,
  Poisson, multinomial, multi-response Gaussian and Cox proportional hazards families
  are supported, with robust Lorenz and smoothed support vector machine (SVM) losses
  available for classification with noisy labels. Dense and sparse design matrices
  are both handled, and the interface mirrors that of the 'glmnet' package.
authors:
- family-names: Barbu
  given-names: Adrian
  email: abarbu@fsu.edu
- family-names: She
  given-names: Yiyuan
repository: https://cran.r-universe.dev
repository-code: https://github.com/barbua/FSA
commit: 81c50becfa91ce2ce6efd0987e92bf751c36fc34
url: https://github.com/barbua/FSA
date-released: '2026-10-21'
contact:
- family-names: Barbu
  given-names: Adrian
  email: abarbu@fsu.edu