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>.

A simulation modeling framework which significantly extends capabilities from the
    'MGDrivE' simulation package via a new mathematical and computational framework based on stochastic Petri nets.
    For more information about 'MGDrivE', see our publication: Sánchez et al. (2019) <doi:10.1111/2041-210X.13318>
    Some of the notable capabilities of 'MGDrivE2' include: incorporation of human populations,
    epidemiological dynamics, time-varying parameters, and a continuous-time simulation
    framework with various sampling algorithms for both deterministic and stochastic interpretations.
    'MGDrivE2' relies on the genetic inheritance structures provided in package 'MGDrivE', so we
    suggest installing that package initially.

Three estimating equation methods are provided in this package for marginal analysis of longitudinal ordinal data with misclassified responses and covariates. 
 The naive analysis which is solely based on the observed data without adjustment may lead to bias.
 The corrected generalized estimating equations (GEE2) method which is unbiased requires the misclassification parameters to be known beforehand. 
 The corrected generalized estimating equations (GEE2) with validation subsample method estimates the misclassification parameters based on a given
 validation set. This package is an implementation 
 of Chen (2013) <doi:10.1002/bimj.201200195>.