mclcar: Estimating Conditional Auto-Regressive (CAR) Models using Monte Carlo Likelihood Methods

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:	0.2-0
Depends:	R (≥ 2.10)
Imports:	spam, rsm, fields, maxLik, nleqslv, spdep, spatialreg
Suggests:	knitr
Published:	2022-01-08
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

Documentation:

Reference manual:	mclcar.pdf
Vignettes:	Introduction to mclcar

Downloads:

Package source:	mclcar_0.2-0.tar.gz
Windows binaries:	r-devel: mclcar_0.2-0.zip, r-release: mclcar_0.2-0.zip, r-oldrel: mclcar_0.2-0.zip
macOS binaries:	r-release (arm64): mclcar_0.2-0.tgz, r-oldrel (arm64): mclcar_0.2-0.tgz, r-release (x86_64): mclcar_0.2-0.tgz, r-oldrel (x86_64): mclcar_0.2-0.tgz
Old sources:	mclcar archive

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