The likelihood of direct CAR models and Binomial and Poisson GLM with latent CAR variables are approximated by the Monte Carlo likelihood. The Maximum Monte Carlo likelihood estimator is found either by an iterative procedure of directly maximising the Monte Carlo approximation or by a response surface design method.Reference for the method can be found in the DPhil thesis in Z. Sha (2016). For application a good reference is R.Bivand et.al (2017) <doi:10.1016/j.spasta.2017.01.002>.
| Version: | 0.1-9 |
| Depends: | R (≥ 2.10) |
| Imports: | spam, rsm, fields, maxLik, nleqslv, spdep |
| Suggests: | knitr |
| Published: | 2018-04-09 |
| Author: | Zhe Sha [aut, cre] |
| Maintainer: | Zhe Sha <zhesha1006 at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| In views: | TimeSeries |
| CRAN checks: | mclcar results |
| Reference manual: | mclcar.pdf |
| Vignettes: |
Introduction to mclcar |
| Package source: | mclcar_0.1-9.tar.gz |
| Windows binaries: | r-devel: mclcar_0.1-9.zip, r-release: mclcar_0.1-9.zip, r-oldrel: mclcar_0.1-9.zip |
| macOS binaries: | r-release: mclcar_0.1-9.tgz, r-oldrel: mclcar_0.1-9.tgz |
| Old sources: | mclcar archive |
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