bspcov - Bayesian Sparse Estimation of a Covariance Matrix

Bayesian estimations of a covariance matrix for multivariate normal data. Assumes that the covariance matrix is sparse or band matrix and positive-definite. Methods implemented include the beta-mixture shrinkage prior (Lee et al. (2022) <doi:10.1016/j.jmva.2022.105067>), screened beta-mixture prior (Lee et al. (2024) <doi:10.1214/24-BA1495>), and post-processed posteriors for banded and sparse covariances (Lee et al. (2023) <doi:10.1214/22-BA1333>; Lee and Lee (2023) <doi:10.1016/j.jeconom.2023.105475>). This software has been developed using funding supported by Basic Science Research Program through the National Research Foundation of Korea ('NRF') funded by the Ministry of Education ('RS-2023-00211979', 'NRF-2022R1A5A7033499', 'NRF-2020R1A4A1018207' and 'NRF-2020R1C1C1A01013338').