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mclcar
View on CRAN: Click
here
Download and install mclcar package within the R console
Install from CRAN:
install.packages("mclcar")
Install from Github:
library("remotes")
install_github("cran/mclcar")
Install by package version:
library("remotes")
install_version("mclcar", "0.2-0")
Attach the package and use:
library("mclcar")
Maintained by
Zhe Sha
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
First Published: 2016-08-04
Latest Update: 2023-08-27
Description:
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) .
How to cite:
Zhe Sha (2016). mclcar: Estimating Conditional Auto-Regressive (CAR) Models using Monte
Carlo Likelihood Methods. R package version 0.2-0, https://cran.r-project.org/web/packages/mclcar. Accessed 24 Apr. 2025.
Previous versions and publish date:
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Other R packages that mclcar depends,
imports, suggests or enhances
Functions, R codes and Examples using
the mclcar R package
Some associated functions: CAR.simLM . OptimMCL.HCAR . OptimMCL . ScotCancer . loglik.dCAR . mcl.HCAR . mcl.dCAR . mcl.glm . mclcar-package . plot.rsmMCL . postZ . rsmMCL . scotplot . sim.HCAR . summary.OptimMCL.HCAR . summary.OptimMCL . summary.rsmMCL .
Some associated R codes: CAR.simLM.R . HCAR.sim.R . OptimMCL.HCAR.R . OptimMCL.R . loglik.dCAR.R . mcl.HCAR.R . mcl.bin.R . mcl.dCAR.R . mcl.glm.R . mcl.pois.R . plot.rsmMCL.R . postZ.R . postZsub.R . rsmMCL.R . rsmSub.R . summary.OptimMCL.HCAR.R . summary.OptimMCL.R . summary.rsmMCL.R . Full mclcar package functions and examples
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