Computes the degrees of freedom of the lasso,
        elastic net, generalized elastic net and adaptive lasso based
        on the generalized path seeking algorithm.  The optimal model
        can be selected by model selection criteria including Mallows'
        Cp, bias-corrected AIC (AICc), generalized cross validation
        (GCV) and BIC.

Provides new functions info(), warn() and error(), similar to message(),
    warning() and stop() respectively. However, the new functions can have a 'level'
    associated with them, so that when executed the global level option determines whether
    they are shown or not. This allows debug modes, outputting more information. The can also
    output all messages to a log file.

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>, Castanza et al.
    2023 <doi:10.1038/s41592-023-02014-7>) as an R data frame. 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.

Plotting functions for mutational signatures and mutational
    spectra, including single base substitutions (SBS), doublet base
    substitutions (DBS), and small insertions and deletions (indels).
    Generates plots similar to those used previously in 
    Alexandrov et al. (2020)<doi:10.1038/s41586-020-1943-3> and
    Rozen et al. (2026)<doi:10.5281/zenodo.18451842>.

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.