Package: ACSSpack 0.0.1.4

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:Ziqian Yang [cre, aut], Kshitij Khare [aut], George Michailidis [aut]

ACSSpack_0.0.1.4.tar.gz
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ACSSpack.pdf |ACSSpack.html
ACSSpack/json (API)

# Install 'ACSSpack' in R:
install.packages('ACSSpack', repos = c('https://nkyangtzeqian.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 200 downloads 3 exports 17 dependencies

Last updated 5 months agofrom:a1e1a547cc. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 02 2024
R-4.5-win-x86_64OKNov 02 2024
R-4.5-linux-x86_64OKNov 02 2024
R-4.4-win-x86_64OKNov 02 2024
R-4.4-mac-x86_64OKNov 02 2024
R-4.4-mac-aarch64OKNov 02 2024
R-4.3-win-x86_64OKNov 02 2024
R-4.3-mac-x86_64OKNov 02 2024
R-4.3-mac-aarch64OKNov 02 2024

Exports:ACSS_gsGLP_gsINSS_gs

Dependencies:codetoolsdoParallelextraDistrforeachglmnetHDCIiteratorslatticeMASSMatrixmvtnormRcppRcppArmadilloRcppEigenshapeslamsurvival