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: 1.0-2
Depends: R (≥ 3.2.0)
Imports: fastGHQuad, stats, bbmle, MASS, VGAM, actuar, Rcpp (≥ 0.12.15), methods, boot, numDeriv
LinkingTo: Rcpp
Suggests: R.rsp, robustbase, lattice, forward
Published: 2018-07-31
Author: Ken Beath [aut, cre]
Maintainer: Ken Beath <ken.beath at mq.edu.au>
Contact: Ken Beath <ken.beath@mq.edu.au>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: NEWS
CRAN checks: robmixglm results

Downloads:

Reference manual: robmixglm.pdf
Vignettes: robmixglm: An R Package for the Analysis of Robust Generalized Linear Models
Package source: robmixglm_1.0-2.tar.gz
Windows binaries: r-devel: robmixglm_1.0-2.zip, r-devel-gcc8: robmixglm_1.0-2.zip, r-release: robmixglm_1.0-2.zip, r-oldrel: robmixglm_1.0-2.zip
OS X binaries: r-release: robmixglm_1.0-2.tgz, r-oldrel: robmixglm_1.0-2.tgz

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