Robust Generalized Linear Models (GLM) using Mixtures R package robmixglm version 1.2-8 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. Software Depends: R(>= 3.2.0) Imports: fastGHQuad, stats, bbmle, VGAM, actuar, Rcpp (>= 0.12.15), methods, boot, numDeriv, parallel, MASS LinkingTo: Rcpp Suggests: R.rsp, robustbase, lattice, forward Ken Beath <ken@kjbeath.id.au> Comprehensive R Archive Network (CRAN) Ken Beath [aut, cre] GPL (>= 2) 2026-07-02 application/tgz https://CRAN.R-project.org/package=robmixglm doi:10.32614/CRAN.package.robmixglm