Package: ACSSpack
Type: Package
Title: ACSS, Corresponding INSS, and GLP Algorithms
Version: 1.0.0.2
Date: 2025-10-10
Authors@R: c(person("Ziqian", "Yang", role = c("cre", "aut"), email = "zi.yang@ufl.edu"), 
           person("Kshitij", "Khare", role = "aut"),
           person("George", "Michailidis", role = "aut"))
Description: 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".
License: GPL-3
Encoding: UTF-8
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)
RoxygenNote: 7.3.3
Depends: R (>= 3.0.2)
LazyData: true
NeedsCompilation: yes
Packaged: 2025-10-11 00:17:38 UTC; yangt
Author: Ziqian Yang [cre, aut],
  Kshitij Khare [aut],
  George Michailidis [aut]
Maintainer: Ziqian Yang <zi.yang@ufl.edu>
Repository: CRAN
Date/Publication: 2025-10-11 04:40:02 UTC
Built: R 4.4.3; x86_64-w64-mingw32; 2025-11-10 04:00:41 UTC; windows
Archs: x64
