Fast application of Continuous Wavelet Transformation ('CWT') on time
    series with special attention to spectroscopy. It is written using
    data.table and 'C++' language and in some functions it is possible to
    use parallel processing to speed-up the computation over samples. Currently, 
    only the second derivative of a Gaussian wavelet function is implemented.

Recent developments in modern coexistence theory have
    advanced our understanding on how species are able to persist and
    co-occur with other species at varying abundances. However, applying
    this mathematical framework to empirical data is still challenging,
    precluding a larger adoption of the theoretical tools developed by
    empiricists. This package provides a complete toolbox for modelling
    interaction effects between species, and calculate fitness and niche
    differences.  The functions are flexible, may accept covariates, and
    different fitting algorithms can be used.  A full description of the
    underlying methods is available in García-Callejas, D., Godoy, O., and
    Bartomeus, I. (2020) <doi:10.1111/2041-210X.13443>.  Furthermore, the
    package provides a series of functions to calculate dynamics for
    stage-structured populations across sites.

Implements 'glmnet'-style complex-valued lasso (CLASSO) and
    complex-valued graphical lasso (CGLASSO) via a pathwise coordinate
    descent algorithm for complex-valued parameters, using an isomorphism
    between complex numbers and 2x2 orthogonal matrices. Also provides a
    full inference pipeline for high-dimensional sparse spectral precision
    matrices, including data-driven bandwidth selection, one-step
    debiasing, asymptotic variance estimation, entry-wise confidence
    regions, and FDR-controlled hypothesis testing. Supporting tools for
    cross-validation, simulation, coefficient extraction, and plotting are
    included. See Deb, Kuceyeski, and Basu (2024)
    <doi:10.48550/arXiv.2401.11128> and Deb, Kim, and Basu (2026)
    <doi:10.48550/arXiv.2606.07986>.