robmixglm: Robust Generalized Linear Models (GLM) using Mixtures

Robust generalized linear models (GLM) using a mixture method, as described in Beath (2018) <doi:10.1080/02664763.2017.1414164>. 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.

Version: 1.2-3
Depends: R (≥ 3.2.0)
Imports: fastGHQuad, stats, bbmle, VGAM, actuar, Rcpp (≥ 0.12.15), methods, boot, numDeriv, parallel, doParallel, foreach, doRNG, MASS
LinkingTo: Rcpp
Suggests: R.rsp, robustbase, lattice, forward
Published: 2022-05-09
DOI: 10.32614/CRAN.package.robmixglm
Author: Ken Beath [aut, cre]
Maintainer: Ken Beath <ken at>
Contact: Ken Beath <>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: NEWS
CRAN checks: robmixglm results


Reference manual: robmixglm.pdf
Vignettes: robmixglm: An R Package for the Analysis of Robust Generalized Linear Models


Package source: robmixglm_1.2-3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): robmixglm_1.2-3.tgz, r-oldrel (arm64): robmixglm_1.2-3.tgz, r-release (x86_64): robmixglm_1.2-3.tgz, r-oldrel (x86_64): robmixglm_1.2-3.tgz
Old sources: robmixglm archive


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