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/>.

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 et al. (2022+) "Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process" <doi:10.1080/01621459.2022.2123336>.

Provides functions which perform Bayesian estimations of 
        a covariance matrix for multivariate normal data. Assumes that
        the covariance matrix is sparse or band matrix and positive-definite. 
        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').

Bayesian inference on the (discrete) power spectrum of time series.