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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".
| Version: | 0.0.1.4 |
| 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: | 2024-07-04 |
| 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 |
| Reference manual: | ACSSpack.pdf |
| Package source: | ACSSpack_0.0.1.4.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 |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.