ACSSpack: ACSS, Corresponding INSS, and GLP Algorithms

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".

Version:	1.0.0.2
Depends:	R (≥ 3.0.2)
Imports:	stats, HDCI (≥ 1.0-2), MASS (≥ 7.3-60), extraDistr (≥ 1.4-4)
LinkingTo:	Rcpp (≥ 1.0.11), RcppArmadillo (≥ 0.12.6.3.0)
Published:	2025-10-11
DOI:	10.32614/CRAN.package.ACSSpack
Author:	Ziqian Yang [cre, aut], Kshitij Khare [aut], George Michailidis [aut]
Maintainer:	Ziqian Yang <zi.yang at ufl.edu>
License:	GPL-3
NeedsCompilation:	yes
CRAN checks:	ACSSpack results

Documentation:

Reference manual:

ACSSpack.html , ACSSpack.pdf

Downloads:

Package source:	ACSSpack_1.0.0.2.tar.gz
Windows binaries:	r-devel: ACSSpack_0.0.1.4.zip, r-release: ACSSpack_0.0.1.4.zip, r-oldrel: ACSSpack_0.0.1.4.zip
macOS binaries:	r-release (arm64): ACSSpack_0.0.1.4.tgz, r-oldrel (arm64): ACSSpack_0.0.1.4.tgz, r-release (x86_64): ACSSpack_0.0.1.4.tgz, r-oldrel (x86_64): ACSSpack_0.0.1.4.tgz
Old sources:	ACSSpack archive

Linking:

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