Position adjustments for 'ggplot2' to implement "visualize as
    you randomize" principles, which can be especially useful when
    plotting experimental data.

Provides tools for analysis blinding in confirmatory research contexts
  by masking and scrambling test-relevant aspects of data. Vector-, data frame-, and
  row-wise operations support blinding for hierarchical and repeated-measures designs.
  For more details see MacCoun and Perlmutter (2015) <doi:10.1038/526187a> and
  Dutilh, Sarafoglou, and Wagenmakers (2019) <doi:10.1007/s11229-019-02456-7>.

Computes the Gaussian variational approximation of the Bayesian 
    empirical likelihood posterior. This is an implementation of the function 
    found in Yu, W., & Bondell, H. D. (2023) <doi:10.1080/01621459.2023.2169701>.

Fit and simulate latent position and cluster models for network data, using a fast Variational Bayes approximation developed in Salter-Townshend and Murphy (2013) <doi:10.1016/j.csda.2012.08.004>.

A Variational Bayesian algorithm for high-dimensional multi-source 
    heterogeneous linear models. More details have been written up in a paper 
    submitted to the journal Statistics in Medicine, and the details of variational
    Bayesian methods can be found in Ray and Szabo (2021) <doi:10.1080/01621459.2020.1847121>.
    It simultaneously performs parameter estimation and variable selection. The 
    algorithm supports two model settings: (1) local models, where variable selection
    is only applied to homogeneous coefficients, and (2) global models, where variable
    selection is also performed on heterogeneous coefficients. Two forms of 
    Spike-and-Slab priors are available: the Laplace distribution and the Gaussian 
    distribution as the Slab component.

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>.