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
Author:	Ken Beath [aut, cre]
Maintainer:	Ken Beath <ken at kjbeath.com.au>
Contact:	Ken Beath <ken@kjbeath.com.au>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
Materials:	NEWS
CRAN checks:	robmixglm results

Documentation:

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

Downloads:

Package source:	robmixglm_1.2-3.tar.gz
Windows binaries:	r-devel: robmixglm_1.2-3.zip, r-release: robmixglm_1.2-3.zip, r-oldrel: robmixglm_1.2-3.zip
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

Linking:

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