R Package
Mhorseshoe package: I am the author of the Mhorseshoe package. It provides functions performing exact and approximate MCMC algorithms for the horseshoe prior in linear regression models, which were proposed by Johndrow et al. (2020).
bspcov package: I am the author of the bspcov package. It provides functions performing Bayesian inference for banded/sparse covariance matrices.
SHT package: I am the author of the SHT package. It provides a collection of statistical hypothesis testing procedures ranging from classical to modern methods for non-trivial settings such as high-dimensional scenarios.
CovTools package: I am the author of the CovTools package. It provides a rich collection of geometric and inferential tools for convenient analysis of covariance structures, topics including distance measures, mean covariance estimator, covariance hypothesis test for one-sample and two-sample cases, and covariance estimation.
LAPinfer package: The LAPinfer package provides R functions for Laplace-based Approximate Posterior (LAP) inference for differential equation models in Dass et al. (2017).
R codes
Bayesian local dependence learning via LANCE prior [R code]: We have proposed a Bayesian local dependence learning via LANCE (LocAl depeNdence CholEsky) prior (Lee and Lin, 2022+). The link provides R codes for the implementation of the cross-validation-based inference using the LANCE prior.
Bayesian hypothesis test via mxPBF [R code]: We have proposed a Bayesian hypothesis test based on the maximum pairwise Bayes factor (mxPBF) (Lee, Lin and Dunson, 2021). The link provides R codes for (i) one-sample covariance test and (ii) diagonality test for covariance matrices.
Joint Bayesian variable and DAG selection [R code]: We have proposed the joint sparse estimation of regression coefficients and the covariance matrix for covariates in a high-dimensional regression model, where the predictors are both relevant to a response variable of interest and functionally related to one another via a Gaussian directed acyclic graph (DAG) model (Cao and Lee, 2021). The link provides R codes for the implementation of the joint Bayesian selection method.