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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)
# update docs
devtools::document()## check package
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 bspcov package from CRAN:
install.packages("bspcov")or the development version from GitHub, by using the function
install_github() from devtools
package:
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)
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.