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ACSSpack  

ACSS, Corresponding INSS, and GLP Algorithms
View on CRAN: Click here


Download and install ACSSpack package within the R console
Install from CRAN:
install.packages("ACSSpack")

Install from Github:
library("remotes")
install_github("cran/ACSSpack")

Install by package version:
library("remotes")
install_version("ACSSpack", "1.0.0.2")



Attach the package and use:
library("ACSSpack")
Maintained by
Ziqian Yang
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-07-04
Latest Update: 2024-07-04
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, preprint) "Bayesian methodology for adaptive sparsity and shrinkage in regression".
How to cite:
Ziqian Yang (2024). ACSSpack: ACSS, Corresponding INSS, and GLP Algorithms. R package version 1.0.0.2, https://cran.r-project.org/web/packages/ACSSpack. Accessed 12 Sep. 2026.
Previous versions and publish date:
0.0.1.4 (2024-07-04 18:40), 1.0.0.2 (2025-10-11 06:40), (2026-07-09 07:56)
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Complete documentation for ACSSpack
Functions, R codes and Examples using the ACSSpack R package
Full ACSSpack package functions and examples
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