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, 
             Generalized Cross Validation and similar, or using iterated 
             nested Laplace approximation for fully Bayesian inference. See 
             Wood (2017) <doi:10.1201/9781315370279> 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'.

Provides a model designed to be a reliable testbed where various gene 
    drive interventions for mosquito-borne diseases control. It is being developed to 
    accommodate the use of various mosquito-specific gene drive systems within a 
    population dynamics framework that allows migration of individuals between patches 
    in landscape. Previous work developing the population dynamics can be found in Deredec et al. 
    (2001) <doi:10.1073/pnas.1110717108> and Hancock & Godfray (2007) <doi:10.1186/1475-2875-6-98>, 
    and extensions to accommodate CRISPR homing dynamics in Marshall et al. (2017) 
    <doi:10.1038/s41598-017-02744-7>.