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# CITATION 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 "ACSSpack" in publications use:'
type: software
license: GPL-3.0-only
title: 'ACSSpack: ACSS, Corresponding INSS, and GLP Algorithms'
version: 1.0.0.2
doi: 10.32614/CRAN.package.ACSSpack
abstract: 'Allow user to run the Adaptive Correlated Spike and Slab (ACSS) algorithm,
  corresponding INdependent Spike and Slab (INSS) algorithm, and Giannone, Lenza and
  Primiceri (GLP) algorithm with adaptive burn-in. All of the three algorithms are
  used to fit high dimensional data set with either sparse structure, or dense structure
  with smaller contributions from all predictors. The state-of-the-art GLP algorithm
  is in Giannone, D., Lenza, M., & Primiceri, G. E. (2021, ISBN:978-92-899-4542-4)
  "Economic predictions with big data: The illusion of sparsity". The two new algorithms,
  ACSS algorithm and INSS algorithm, and the discussion on their performance can be
  seen in Yang, Z., Khare, K., & Michailidis, G. (2024, submitted to Journal of Business
  & Economic Statistics) "Bayesian methodology for adaptive sparsity and shrinkage
  in regression".'
authors:
- family-names: Yang
  given-names: Ziqian
  email: zi.yang@ufl.edu
- family-names: Khare
  given-names: Kshitij
- family-names: Michailidis
  given-names: George
repository: https://cranhaven.r-universe.dev
commit: d5313a5579255679a92762fb4c8b073395a21046
date-released: '2025-10-10'
contact:
- family-names: Yang
  given-names: Ziqian
  email: zi.yang@ufl.edu