Package: ACSSpack 0.0.1.4
Ziqian Yang
ACSSpack: ACSS, Corresponding ACSS, and GLP Algorithm
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".
Authors:
ACSSpack_0.0.1.4.tar.gz
ACSSpack_0.0.1.4.tar.gz(r-4.5-noble)ACSSpack_0.0.1.4.tar.gz(r-4.4-noble)
ACSSpack_0.0.1.4.tgz(r-4.4-emscripten)ACSSpack_0.0.1.4.tgz(r-4.3-emscripten)
ACSSpack.pdf |ACSSpack.html✨
ACSSpack/json (API)
| # Install 'ACSSpack' in R: |
| install.packages('ACSSpack', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- Econ_data - Economic data from the GLP paper
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 5 months agofrom:a1e1a547cc. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 02 2024 |
| R-4.5-linux-x86_64 | OK | Nov 02 2024 |
Dependencies:codetoolsdoParallelextraDistrforeachglmnetHDCIiteratorslatticeMASSMatrixmvtnormRcppRcppArmadilloRcppEigenshapeslamsurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| ACSS algorithm | ACSS_gs |
| Economic data from the GLP paper | Econ_data |
| GLP algorithm | GLP_gs |
| INSS algorithm | INSS_gs |
