An R package for Bayesian Sparse Estimation of a Covariance Matrix

To build the package from source, you need to have the following:

# lock the renv
pkgs <- c("...")
renv::snapshot(packages = pkgs)

# R CMD check .
VERSION=$(git describe --tags | sed 's/v//g')

## build manual
R CMD Rd2pdf --force --no-preview -o bspcov-manual.pdf .

## build package
sed -i '' "s/Version: [^\"]*/Version: ${VERSION}/g" "DESCRIPTION"
R CMD build .

You can install the development version of bspcov from GitHub with:

# install.packages("devtools")
devtools::install_github("statjs/bspcov", ref = "main")

Lee, Jo, and Lee (2022). The beta-mixture shrinkage prior for sparse covariances with posterior near-minimax rate, Journal of Multivariate Analysis, 192, 105067.
Lee, Jo, and Lee (2023+). Scalable and optimal Bayesian inference for sparse covariance matrices via screened beta-mixture prior.
Lee, Lee, and Lee (2023+). Post-processes posteriors for banded covariances, Bayesian Analysis, DOI: 10.1214/22-BA1333.
Lee and Lee (2023). Post-processed posteriors for sparse covariances, Journal of Econometrics, 236(3), 105475.

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)
(RS-2023-00211979, NRF-2022R1A5A7033499, NRF-2020R1A4A1018207, and NRF-2020R1C1C1A01013338)