Software
R Package
bspcov package: I am the author of bspcov package. It provides functions performing Bayesian inference for banded/sparse covariance matrices.
SHT package: I am the author of 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 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: 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.