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.


News

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

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install.packages("robmixglm")

1.0-2 by Ken Beath, 8 months ago


Browse source code at https://github.com/cran/robmixglm


Authors: Ken Beath [aut, cre]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports fastGHQuad, stats, bbmle, MASS, VGAM, actuar, Rcpp, methods, boot, numDeriv

Suggests R.rsp, robustbase, lattice, forward

Linking to Rcpp


See at CRAN