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" (2026) 
    <doi:10.1287/opre.2023.0598>. The algorithm uses an iterative deflation 
    heuristic with a truncated power method applied at each iteration to compute 
    sparse principal components with controlled sparsity.

Analyse disability course in multiple sclerosis (MS) from
    longitudinal data. The package provides a flexible framework for
    identifying disability events under user-specified criteria, allowing
    adaptation to different study designs and endpoints. Tools are included
    to facilitate transparent and reproducible reporting of the settings
    used in the analysis. For an introduction to the package and illustrative
    applications, see Montobbio et al. (2024) <doi:10.1177/13524585241243157>.

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.

Perform library searches against electron ionization mass spectral
    databases using either the API provided by 'MS Search' software
    (<https://chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:nistlibs>) or
    custom implementations of the Identity and Similarity algorithms.