Implements adaptive generalized Bayesian quantile regression with quantile-specific learning rates, HAC-based calibration, Gibbs posterior simulation, posterior summaries, predictive evaluation, and visualization tools. The package builds on the generalized Bayesian composite quantile regression framework of Hardy and Korobilis (2026) <doi:10.2139/ssrn.6618603> by allowing learning rates to vary across quantile levels. The implementation is designed for empirical work with small and moderate time-series samples where posterior calibration and tail-specific inference are important.

Implements anchored geodesic component analysis for multivariate
    extremes. The package provides Pareto and rank-Pareto marginal
    standardization, top-k angular extraction, anchored eigensolutions,
    reconstruction, diagnostics, nonparametric bootstrap tools, and plotting
    methods for benchmark-relative angular variation.

Calculate 'ActiGraph' counts from the X, Y, and Z axes of a triaxial 
    accelerometer. This work was inspired by Neishabouri et al. who published the 
    article "Quantification of Acceleration as Activity Counts in 'ActiGraph' Wearables" 
    on February 24, 2022. The link to the article (<https://pubmed.ncbi.nlm.nih.gov/35831446>) 
    and 'python' implementation of this code (<https://github.com/actigraph/agcounts>).

Tools for the analysis of growth data: to extract an 
    LMS table from a gamlss object, to calculate the standard 
    deviation scores and its inverse, and to superpose two wormplots 
    from different models. The package contains a some varieties of 
    reference tables, especially for The Netherlands.

Implements the Age Band Decomposition (ABD) method for 
 standardizing tree ring width data while preserving both low and high frequency 
 variability. Unlike traditional detrending approaches that can distort long term 
 growth trends, ABD decomposes ring width series into multiple age classes, detrends 
 each class separately, and then recombines them to create standardized chronologies. 
 This approach improves the detection of growth signals linked to past climatic and 
 environmental factors, making it particularly valuable for dendroecological and 
 dendroclimatological studies. The package provides functions to perform ABD-based 
 standardization, compare results with other common methods (e.g., BAI, C method, RCS), 
 and facilitate the interpretation of growth patterns under current and future climate 
 variability.