A Bayesian Repulsive Biclustering Model for Periodontal Data
The BAREB package: summary information
const
The function to get the kernel function value
likeli_theta
Function to update the number of site level clusters for one patient l...
Function to update the number of site level clusters for one patient l...
update_RJ
update_sigma_squre
update_theta_beta
update_theta_gamma
update_w_beta
update_w
Function to update c in missingess model
Function to update patient level linear coefficients in the BAREB mode...
Function to obtain the kernel matrix of the determinantal point proces...
Function to update patient level clustering in the BAREB model
Function to update site level linear coefficients in the BAREB model
Function to update estimated mean CAL values based on current paramete...
Function to update mean latent values for missingness model
Function to update site level clustering in the BAREB model
Function to generate new latent values for missingness model
Simultaneously clusters the Periodontal diseases (PD) patients and their tooth sites after taking the patient- and site-level covariates into consideration. 'BAREB' uses the determinantal point process (DPP) prior to induce diversity among different biclusters to facilitate parsimony and interpretability. Essentially, 'BAREB' is a cluster-wise linear model based on Yuliang (2020) <doi:10.1002/sim.8536>.