Provides high-performance tools for macroeconomic trend
    extraction and filtering, specifically designed to solve the end-point
    problem in real-time. Implements the MacroBoost Hybrid (MBH) filter
    using penalized P-splines and gradient boosting. Unlike the standard
    Hodrick-Prescott filter, 'MacroFilters' utilizes component-wise
    L2-boosting with robust loss functions (Huber) to handle extreme
    transient shocks (e.g., COVID-19) without inducing spurious trend
    shifts. The algorithm includes an automated two-layer diagnostic stage
    for unit roots and structural breaks, optimized via corrected AICc for
    computational efficiency. Methodology detailed in Kinel (2026)
    <doi:10.2139/ssrn.6371138>.

Use standard genomics file format (BED) and a table of orthologs to 
 illustrate synteny conservation at the genome-wide scale. 
 Significantly conserved linkage groups are identified as described in Simakov et al. (2020) <doi:10.1038/s41559-020-1156-z> 
 and displayed on an Oxford Grid (Edwards (1991) <doi:10.1111/j.1469-1809.1991.tb00394.x>) or a chord diagram as in Simakov et al. (2022) <doi:10.1126/sciadv.abi5884>. 
 The package provides a function that uses a network-based greedy algorithm to find communities (Clauset et al. (2004) <doi:10.1103/PhysRevE.70.066111>) 
 and so automatically order the chromosomes on the plot to improve interpretability.

Includes functions  for calculating basic indices of macrozoobenthos for water quality and is designed to provide researchers and environmental professionals with a comprehensive tool for evaluating the ecological health of aquatic ecosystems.The package is based on the following references: Paisley, M. F., Trigg, D. J. and Walley, W. J. (2014)<doi:10.1002/rra.2686>. Arslan, N., Salur, A., Kalyoncu, H. et al.(2016)  <doi:10.1515/biolog-2016-0005>. Hilsenhoff W.L. (1987). Hilsenhoff. W.L. (1988)  Barbour, M.T., Gerritsen, J., Snyder, B.D., and Stribling, J.B. (1999).

Provides methods and classes for adding m-activation ("multiplicative activation") layers to MLR or multivariate logistic regression models.  M-activation layers created in this library detect and add input interaction (polynomial) effects into a predictive model.  M-activation can detect high-order interactions -- a traditionally non-trivial challenge.  Details concerning application, methodology, and relevant survey literature can be found in this library's vignette, "About."