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: | 1.2-5 |
| 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: | 2025-10-31 |
| 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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