Procedures to simulate, estimate and diagnose MGARCH
    processes of BEKK and multivariate GJR (bivariate asymmetric GARCH
    model) specification.

Compute the multiple Grubbs-Beck low-outlier test on positively distributed
 data and utilities for noninterpretive U.S. Geological Survey annual peak-streamflow
 data processing discussed in Cohn et al. (2013) and
 England et al. (2017) <doi:10.3133/tm4B5>. Other utilities for working with peak streamflow
 are provided.

Multiscale Graph Correlation (MGC) is a framework developed by Vogelstein et al. (2019) <DOI:10.7554/eLife.41690> that extends global correlation procedures to be multiscale; consequently, MGC tests typically require far fewer samples than existing methods for a wide variety of dependence structures and dimensionalities, while maintaining computational efficiency. Moreover, MGC provides a simple and elegant multiscale characterization of the potentially complex latent geometry underlying the relationship.

Generalized additive (mixed) models, some of their extensions and 
             other generalized ridge regression with multiple smoothing 
             parameter estimation by (Restricted) Marginal Likelihood, 
             Cross Validation and similar, or using iterated nested Laplace
	     approximation for fully Bayesian inference. See Wood (2025)
	     <doi:10.1146/annurev-statistics-112723-034249> for an overview.
	     Includes a gam() function, a wide variety of smoothers, 'JAGS'
	     support and distributions beyond the exponential family.

Extension of the 'mgcv' package, providing visual tools for Generalized Additive Models that exploit the additive structure of such models, scale to large data sets and can be used in conjunction with a wide range of response distributions. The focus is providing visual methods for better understanding the model output and for aiding model checking and development beyond simple exponential family regression. The graphical framework is based on the layering system provided by 'ggplot2'.