[aut, cre]Implementation of Expectation Maximization (EM) regression of general linear models. The package currently supports Poisson and Logistic regression with variable weights, with underlying theory included in the vignettes. New users are recommended to look at the em.glm() and small.em() functions - the outputs of which are supported by AIC(), BIC(), and logLik() calls. Several plot functions have been included for useful diagnostics and model exploration. Methods are based on the theory of Dempster et al (1977, ISBN:00359246), and follow the methods of Hastie et al. (2009) <doi:10.1007/978-0-387-21606-5_7> and A. Zeileis et al (2017) <doi:10.18637/jss.v027.i08>.
| Version: | 0.1.2 |
| Depends: | R (≥ 2.10) |
| Imports: | MASS, pracma, stats, pander, pROC, AER |
| Suggests: | spelling, knitr, rmarkdown |
| Published: | 2019-07-04 |
| Author: | Robert M. Cook
[aut, cre] |
| Maintainer: | Robert M. Cook <robert.cook at bcu.ac.uk> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Language: | en-US |
| Materials: | README |
| CRAN checks: | emax.glm results |
| Reference manual: | emax.glm.pdf |
| Vignettes: |
em.glm vignette The EM GLM algorithm - Proof of predicted values Residual Theory Warm-up and exposure in the EM algorithm |
| Package source: | emax.glm_0.1.2.tar.gz |
| Windows binaries: | r-devel: emax.glm_0.1.2.zip, r-devel-gcc8: emax.glm_0.1.2.zip, r-release: emax.glm_0.1.2.zip, r-oldrel: emax.glm_0.1.2.zip |
| OS X binaries: | r-release: emax.glm_0.1.2.tgz, r-oldrel: emax.glm_0.1.2.tgz |
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