A variable selection tool for multivariate normal variables with missing-at-random values using Bayesian Hierarchical Model.
    Visualization functions show the posterior distribution of gamma (inclusion variables) and beta (coefficients). 
    Users can also visualize the heatmap of the posterior mean of covariance matrix. 
    Kim, T. Nicolae, D. (2019) <https://github.com/tk382/MMVBVS/blob/master/workingpaper.pdf>.
    Guan, Y. Stephens, M. (2011) <doi:10.1214/11-AOAS455>.

The first day of any MMWR week is Sunday.
    MMWR week numbering is sequential beginning with 1
    and incrementing with each week to a maximum of 52
    or 53. MMWR week #1 of an MMWR year is the first week
    of the year that has at least four days in the calendar
    year. This package provides functionality to convert
    Dates to MMWR day, week, and year and the reverse.

Density computation, random matrix generation and maximum likelihood estimation of the matrix normal distribution. References: Pocuca N., Gallaugher M. P., Clark K. M. & McNicholas P. D. (2019). Assessing and Visualizing Matrix Variate Normality. <doi:10.48550/arXiv.1910.02859> and the relevant wikipedia page.

Clustering of data under a non-ignorable missingness mechanism. Clustering is achieved by a semi-parametric mixture model and missingness is managed by using the pattern-mixture approach. More details of the approach are available in Du Roy de Chaumaray et al. (2020) <arXiv:2009.07662>.

Diagnostic tools as residual analysis, global, 
    local and total-local influence for the multivariate model 
    from the random intercept Poisson generalized log gamma model 
    are available in this package. Including also, the estimation 
    process by maximum likelihood method, for details see 
    Fabio, L. F; Villegas, C. L.; Carrasco, J.M.F and de Castro, M. (2021) 
    <doi:10.1080/03610926.2021.1939380>.

Interactions between different biological entities are crucial for the function of biological systems. 
    In such networks, nodes represent biological elements, such as genes, proteins and microbes, and their interactions can be defined by edges, which can be either binary or weighted.
    The dysregulation of these networks can be associated with different clinical conditions such as diseases and response to treatments. 
    However, such variations often occur locally and do not concern the whole network. 
    To capture local variations of such networks, we propose multiplex network differential analysis (MNDA). 
    MNDA allows to quantify the variations in the local neighborhood of each node (e.g. gene) between the two given clinical states, and to test for statistical significance of such variation.
    Yousefi et al. (2023) <doi:10.1101/2023.01.22.525058>.