Allows for the computation of mSHAP values on
    two-part models as proposed  by Matthews, S. and Hartman, B. 
    (2021) <arXiv:2106.08990>.
    Also contains functions for simple plotting of
    the results (or any SHAP values). For information about the
    TreeSHAP algorithm that mSHAP builds on, see Lundberg, S.M.,
    Erion, G., Chen, H., DeGrave, A., Prutkin, J.M., Nair, B., Katz,
    R., Himmelfarb, J., Bansal, N., Lee, S.I. (2020)
    <doi:10.1038/s42256-019-0138-9>.

The Molecular Signatures Database ('MSigDB') is one of the most widely 
    used and comprehensive databases of gene sets for performing gene set 
    enrichment analysis <doi:10.1016/j.cels.2015.12.004>. The 'msig' package provides 
    you with powerful, easy-to-use and flexible query functions for the 'MsigDB' 
    database.
        There are 2 query modes in the 'msig' package: online query and local query. 
    Both queries contain 2 steps: gene set name and gene.
        The online search is divided into 2 modes: registered search and 
    non-registered browse. For registered search, email that you registered should 
    be provided.
        Local queries can be made from local database, which can be updated by msig_update() function.

Provides the 'Molecular Signatures Database' (MSigDB) gene
    sets typically used with the 'Gene Set Enrichment Analysis' (GSEA)
    software (Subramanian et al. 2005 <doi:10.1073/pnas.0506580102>,
    Liberzon et al. 2015 <doi:10.1016/j.cels.2015.12.004>) in a standard R
    data frame with key-value pairs. The package includes the human genes
    as listed in MSigDB as well as the corresponding symbols and IDs for
    frequently studied model organisms such as mouse, rat, pig, fly, and
    yeast.

Traditional methods typically detect breakpoints from individual signals, which means that when applied separately to multiple signals, the breakpoints are not aligned. 
    However, this package implements a common breakpoint detection approach for multiple piecewise constant signals, resulting in increased detection sensitivity and specificity. 
    By employing various techniques, optimal performance is ensured, and computation is accelerated. We hope that this package will be beneficial for researchers in signal processing, bioinformatics, economy, and other related fields.
    The segmentation(), lambda_estimator() functions are the main functions of this package.