The VGAM package for R fits vector generalized linear and additive models (VGLMs/VGAMs), as well as reduced-rank VGLMs (RR-VGLMs) and quadratic RR-VGLMs (QRR-VGLMs), and can be obtained below. It is a general program for maximum likelihood estimation, and centers on the six S functions vglm(), vgam(), rrvglm(), cqo(), cao() and rcim().
VGAM can fit regression models to the following data types:
Cauchy (1- and 2-parameter) | exponential | geometric (and truncated geometric) |
normal | negative binomial | Weibull (and truncated Weibull) |
zeta | logarithmic series | inverse Gaussian |
Student t | chisquare | Pareto (and truncated Pareto) |
Haight's zeta | Erlang | Borel-Tanner |
log-gamma | generalized Poisson | inverse binomial |
hyperbolic secant | reciprocal inverse Gaussian | univariate simplex |
logistic (1- and 2-parameter) | gamma | beta (2 parameterizations) |
lognormal | skew normal | Leipnik |
Levy | Weibull | generalized beta II |
Singh-Maddala | Dagum | Fisk |
beta II | Lomax | inverse Lomax |
paralogistic | inverse paralogistic | Rayleigh |
Maxwell | Nakagami | beta-prime |
McKay's bivariate gamma | generalized gamma | Freund (1961) bivariate exponential |
F distribution | hypergeometric | McCullagh's (1989) distribution |
Frechet | 4-parameter bilogistic | Frank's bivariate distribution |
von Mises | Birnbaum-Saunders | generalized beta (Libby and Novick, 1982) |
Zipf distribution | sequential binomial | double exponential binomial |
Plackett's bivariate distribution | Rice | Inverse binomial |
Kumaraswamy | Folded normal | Felix |
Asymmetric Laplace | Makeham | Perks |
Lindley | Gompertz | Gumbel-II |
Bivariate normal | Bivariate Student-t | Bivariate Clayton copula |
Bivariate Frank copula | Ali-Mikhail-Haq | Farlie-Gumbel-Morgenstern |
Here is the VGAM reference card.pdf (192 KB) (Last updated: 2020-10-24).
For a summary of VGAM
click here
Here is a larger summary of VGAM:
VGAM.pdf (2.6 MB).
For the very latest (prerelease) version of VGAM
click here
Some data sets have now been placed into another
package called VGAMdata.
See here
or
CRAN.
There is also another
package called VGAMextra;
see
CRAN.
VGAMs are described in Yee and Wild (1996), JRSSB 58: 481-493 and Yee (2015), "Vector Generalized Linear and Additive Models: With an Implementation in R", Springer, NY:USA. If you use this software please quote from this BIBTEX file.
The software, which is free, consists of R, FORTRAN 77 and ANSI C functions. Currently VGAM is available at CRAN. It runs under R 3.4.0 or higher. VGAM is written in Version 4 of the S language (Chambers, 1998), also known as S4.