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>.

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>.

Time series decomposition for univariate time series using the
    "Verallgemeinerte Berliner Verfahren" (Generalized Berlin Method) as
    described in 'Kontinuierliche Messgrößen und Stichprobenstrategien in
    Raum und Zeit mit Anwendungen in den Natur-, Umwelt-, Wirtschafts-
    und Finanzwissenschaften', by
    Hebbel and Steuer, Springer Berlin Heidelberg, 2022
    <doi:10.1007/978-3-662-65638-9>, or
    'Decomposition of Time Series using the Generalised Berlin 
    Method (VBV)' by Hebbel and Steuer, in Jan Beran, Yuanhua Feng, Hartmut
    Hebbel (Eds.): Empirical Economic and Financial Research - Theory,
    Methods and Practice, Festschrift in Honour of Prof. Siegfried Heiler.
    Series: Advanced Studies in Theoretical and Applied Econometrics.
    Springer 2014, p. 9-40.