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 |
Documentation:
Downloads:
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