Provides a formula interface for the 'glmnet' package for
    elasticnet regression, a method for cross-validating the alpha parameter,
    and other quality-of-life tools.

Contains all the data and functions used in Generalized
    Linear Models, 2nd edition, by Jeff Gill and Michelle Torres. Examples to create
    all models, tables, and plots are included for each data set.

A path-following algorithm for L1 regularized generalized linear models and Cox proportional hazards model.

Provides a function for fitting a penalized constrained  continuation ratio model using the glmpath algorithm and methods for  extracting coefficient estimates, predicted class, class probabilities, and plots as described by Archer and Williams (2012) <doi:10.1002/sim.4484>.

Implements a generalized version of principal components analysis
    (GLM-PCA) for dimension reduction of non-normally distributed data such as
    counts or binary matrices.
    Townes FW, Hicks SC, Aryee MJ, Irizarry RA (2019) <doi:10.1186/s13059-019-1861-6>.
    Townes FW (2019) <arXiv:1907.02647>.

In practical applications, the assumptions underlying generalized linear models frequently face violations, including incorrect specifications of the outcome variable's distribution or omitted predictors. These deviations can render the results of standard generalized linear models unreliable. As the sample size increases, what might initially appear as minor issues can escalate to critical concerns. To address these challenges, we adopt a permutation-based inference method tailored for generalized linear models. This approach offers robust estimations that effectively counteract the mentioned problems, and its effectiveness remains consistent regardless of the sample size.

Data sets from the book Generalized Linear Models with Examples in R by Dunn and Smyth.

Creates ideal data for all distributions in the generalized
    linear model framework.

Fit spatio-temporal models within a (double) generalized linear modelling framework. The package includes functions for estimation, simulation and inference.