Robust Generalized Linear Models (GLM) using Mixtures

Robust generalized linear models (GLM) using a mixture method, as described in Beath (2018) . This assumes that the data are a mixture of standard observations, being a generalised linear model, and outlier observations from an overdispersed generalized linear model. The overdispersed linear model is obtained by including a normally distributed random effect in the linear predictor of the generalized linear model.


Changes in robmixglm version 1.0-2

o added extra sentences in description

o changed dontrun to donttest

o changed several examples to allow running as part of check

Changes in robmixglm version 1.0-1

o first release

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


1.2-2 by Ken Beath, 7 months ago

Browse source code at

Authors: Ken Beath [aut, cre]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports fastGHQuad, stats, bbmle, VGAM, actuar, Rcpp, methods, boot, numDeriv, parallel, doParallel, foreach, doRNG, MASS

Suggests R.rsp, robustbase, lattice, forward

Linking to Rcpp

See at CRAN