Automated model selection and model-averaging. Provides a
        wrapper for glm and other functions, automatically generating
        all possible models (under constraints set by the user) with
        the specified response and explanatory variables, and finding
        the best models in terms of some Information Criterion (AIC,
        AICc or BIC). Can handle very large numbers of candidate
        models. Features a Genetic Algorithm to find the best models
        when an exhaustive screening of the candidates is not feasible.

Variable selection deviation (VSD) measures and instability tests for high-dimensional model selection methods such as LASSO, SCAD and MCP, etc., to decide whether the sparse patterns identified by those methods are reliable.

Extended techniques for generalized linear models (GLMs), especially for binary responses,
             including parametric links and heteroscedastic latent variables.

Provides diagnostic graphic tools for GLMs, beta-binomial regression model (estimated by 'VGAM' package), beta regression model (estimated by 'betareg' package) and negative binomial regression model (estimated by 'MASS' package). Since most of functions implemented in 'glmxdiag' already exist in other packages, the aim is to provide the user unique functions that work on almost all regression models previously specified. Details about some of the implemented functions can be found in Brown (1992) <doi:10.2307/2347617>, Dunn and Smyth (1996) <doi:10.2307/1390802>, O'Hara Hines and Carter (1993) <doi:10.2307/2347405>, Wang (1985) <doi:10.2307/1269708>.

Second-order summary statistics K- and pair-correlation functions describe
    interactions in point pattern data. This package provides computations to estimate those
    statistics on inhomogeneous point processes, using the methods of
    in T Shaw, J Møller, R Waagepetersen, 2020 <doi:10.48550/arXiv.2004.00527>.

It provides more configurations on the option values such as validation
    and filtering on the values, making options invisible or private.