penalized: AMATLABToolbox for Fitting Generalized Linear Models with Penalties

Penalized likelihood regression for generalized linear models with nonquadratic penalties

One popular method for fitting a regression function is regularization: minimize an objective function which enforces a roughness penalty in addition to coherence with the data. This is the case when formulating penalized likelihood regression for exponential families. Most smoothing methods employ quadratic penalties, leading to linear estimates, and are in general incapable of recovering disc...

Penalized Regression Methods with Application to Generalized Linear Models, Generalized Additive Models, and Smoothing

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glm2: Fitting Generalized Linear Models with Convergence Problems

The R function glm uses step-halving to deal with certain types of convergence problems when using iteratively reweighted least squares to fit a generalized linear model. This works well in some circumstances but non-convergence remains a possibility, particularly with a nonstandard link function. In some cases this is because step-halving is never invoked, despite a lack of convergence. In oth...

hglm: A Package for Fitting Hierarchical Generalized Linear Models

We present the hglm package for fitting hierarchical generalized linear models. It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the model.

Fitting Generalized Linear Mixed Models For Point-Referenced Spatial Data