Multivariate Analysis methods and data sets used
    in John Marden's book Multivariate Statistics: Old School (2015) <ISBN:978-1456538835>.
    This also serves as a companion package for the 
    STAT 571: Multivariate Analysis course offered by the Department of Statistics
    at the University of Illinois at Urbana-Champaign ('UIUC').

Enable operationalized evaluation of disease outcomes in
    multiple sclerosis. ‘MSoutcomes’ requires longitudinally recorded
    clinical data structured in long format. The package is based on the
    research developed at Clinical Outcomes Research unit (CORe),
    University of Melbourne and Neuroimmunology Centre, Royal Melbourne
    Hospital. Kalincik et al. (2015) <doi:10.1093/brain/awv258>.
    Lorscheider et al.  (2016) <doi:10.1093/brain/aww173>. Sharmin et al.
    (2022) <doi:10.1111/ene.15406>.  Dzau et al. (2023)
    <doi:10.1136/jnnp-2023-331748>.

Implements an algorithm for computing multiple sparse principal 
    components of a dataset. The method is based on Cory-Wright and Pauphilet 
    "Sparse PCA with Multiple Principal Components" (2022) 
    <doi:10.48550/arXiv.2209.14790>. The algorithm uses an iterative deflation 
    heuristic with a truncated power method applied at each iteration to compute 
    sparse principal components with controlled sparsity.

Given the maximum available sample size (N) for an experiment, and the target levels of Type I 
             and II error probabilities, this package designs a modified SPRT (MSPRT). For any designed MSPRT 
             the package can also obtain its operating characteristics and implement the test for a given 
             sequentially observed data. The MSPRT is defined in a manner very similar to Wald's initial proposal. 
             The proposed test has shown evidence of reducing the average sample size required to perform 
             statistical hypothesis tests at specified levels of significance and power. Currently, the package
             implements one-sample proportion tests, one and two-sample z tests, and one and two-sample 
             t tests. A brief user guidance for this package is provided below. One can also refer to the 
             supplemental information for the same.