Bayes screening and model discrimination follow-up designs.

Functions for training an optimal decision tree classifier, making predictions and generating latex code for plotting. Works for two-class and multi-class classification problems. The algorithm seeks the optimal Boolean rule consisting of multiple variables to split a node, resulting in shorter trees. Use bsnsing() to build a tree, predict() to make predictions and plot() to plot the tree into latex and PDF. See Yanchao Liu (2022) <arXiv:2205.15263> for technical details. Source code and more data sets are at <https://github.com/profyliu/bsnsing/>.

Provides an S4 class and methods for analyzing microbial social
    behavior in bacterial consortia. Includes growth parameter extraction,
    social behavior classification (cooperators/cheaters/neutrals),
    diversity effect analysis, consortium assembly path finding, and
    stability analysis via coefficient of variation. Methods are described
    in Purswani et al. (2017) <doi:10.3389/fmicb.2017.00919>.

The purpose of this package is to fit the three Spatial Econometric Models proposed
  in Anselin (1988, ISBN:9024737354) in the homoscedastic and the heteroscedatic case. The fit is made through MCMC algorithms
  and observational working variables approach.

Gibbs sampling for Bayesian spatial blind source separation (BSP-BSS). BSP-BSS is designed for spatially dependent signals in high dimensional and large-scale data, such as neuroimaging. The method assumes the expectation of the observed images as a linear mixture of multiple sparse and piece-wise smooth latent source signals, and constructs a Bayesian nonparametric prior by thresholding Gaussian processes. Details can be found in our paper: Wu, B., Guo, Y., & Kang, J. (2024). Bayesian spatial blind source separation via the thresholded gaussian process. Journal of the American Statistical Association, 119(545), 422-433.

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').