VGAM:Vector Generalized Linear and Additive Models
An implementation of about 6 major classes of statistical regression models. The central algorithm is Fisher scoring and
iterative reweighted least squares. At the heart of this
package are the vector generalized linear and additive model
(VGLM/VGAM) classes. VGLMs can be loosely thought of as
multivariate GLMs. VGAMs are data-driven VGLMs that use
smoothing. The book "Vector Generalized Linear and Additive
Models: With an Implementation in R" (Yee, 2015)
<DOI:10.1007/978-1-4939-2818-7> gives details of the
statistical framework and the package. Currently only
fixed-effects models are implemented. Many (100+) models and
distributions are estimated by maximum likelihood estimation
(MLE) or penalized MLE. The other classes are RR-VGLMs
(reduced-rank VGLMs), quadratic RR-VGLMs, doubly constrained
RR-VGLMs, quadratic RR-VGLMs, reduced-rank VGAMs, RCIMs
(row-column interaction models)---these classes perform
constrained and unconstrained quadratic ordination (CQO/UQO)
models in ecology, as well as constrained additive ordination
(CAO). Hauck-Donner effect detection is implemented. Note that
these functions are subject to change; see the NEWS and
ChangeLog files for latest changes.
