Identification of Latent Patient Phenotype from Electronic Health Records (EHR) Data using Variational Bayes Gaussian Mixture Model for Latent Class Analysis and Variational Bayes regression for Biomarker level shifts, both implemented by Coordinate Ascent Variational Inference algorithms.  Variational methods are used to enable Bayesian analysis of very large Electronic Health Records data. For VB GMM details see Bishop (2006,ISBN:9780-387-31073-2). For Logistic VB see Jaakkola and Jordan (2000) <doi:10.1023/A:1008932416310>. Please see preprint of JSS-submitted paper <doi:10.48550/arXiv.2512.14272>.

Add publication-quality custom legends with vertical brackets. Designed for displaying statistical comparisons between groups,
    commonly used in scientific publications for showing significance levels.
    Features include adaptive positioning, automatic bracket spacing for overlapping
    comparisons, font family inheritance, and support for asterisks, p-values,
    or custom labels. Compatible with 'ggplot2' graphics.

Contains functions for a variational Bayesian method for sparse PCA proposed by Ning (2020) <arXiv:2102.00305>. There are two algorithms: the PX-CAVI algorithm (if assuming the loadings matrix is jointly row-sparse) and the batch PX-CAVI algorithm (if without this assumption). The outputs of the main function, VBsparsePCA(), include the mean and covariance of the loadings matrix, the score functions, the variable selection results, and the estimated variance of the random noise.

Vector binary tree provides a new data structure, to
 make your data visiting and management more efficient. If the
 data has structured column names, it can read these names and
 factorize them through specific split pattern, then build the mappings
 within double list, vector binary tree, array and tensor mutually, through
 which the batched data processing is achievable easily. The methods of
 array and tensor are also applicable. Detailed methods are described in
 Chen Zhang et al. (2020) <doi:10.35566/isdsa2019c8>.