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ACSSpack
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", "0.0.1.4")
Attach the package and use:
library("ACSSpack")
Maintained by
Ziqian Yang
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.ACSSpack . ACSSpack results . ACSSpack.pdf . ACSSpack_0.0.1.4.tar.gz . ACSSpack_0.0.1.4.zip . ACSSpack_0.0.1.4.zip . ACSSpack_0.0.1.4.zip . ACSSpack_0.0.1.4.tgz . ACSSpack_0.0.1.4.tgz . ACSSpack_0.0.1.4.tgz . ACSSpack_0.0.1.4.tgz . ACSSpack_0.0.1.4.tgz . ACSSpack_0.0.1.4.tgz . https://CRAN.R-project.org/package=ACSSpack .
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 ACSS, and GLP Algorithm. R package version 0.0.1.4, https://cran.r-project.org/web/packages/ACSSpack. Accessed 23 Feb. 2025.
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