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: | 1.2-4 | 
| 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: | 2024-09-27 | 
| DOI: | 10.32614/CRAN.package.robmixglm | 
| Author: | Ken Beath [aut, cre] | 
| Maintainer: | Ken Beath  <ken at kjbeath.id.au> | 
| Contact: | Ken Beath <ken@kjbeath.id.au> | 
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] | 
| NeedsCompilation: | yes | 
| Materials: | NEWS | 
| CRAN checks: | robmixglm results | 
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