Title: Multiple matrices heatmap visualization
Authors@R: c(person("Thierry","Chenard",role = "aut", email =
= c("aut","cre"), email = "rafael.najmanovich@usherbrooke.ca"))
Description: mcheatmaps serves to visualize multiple different symmetric matrices and matrix clusters in a single figure using a dendogram, two half matrices and various color labels.

Description: mchof (read: mc'hoff) provides convenient, consistent
parallel implementations of several commonly used higher-order
functions found in base R. Excellent parallel implementations
of map-like functions exist in packages such as plyr and
parallel, giving users better efficiency with little work on
their part. This package aims to extend this speedup to people
who use functions like Filter or Reduce in their code.
SystemRequirements: POSIX-compliant OS (not Windows)
Collate: 'filter.R' 'call_mclapply.R' 'reduce.R' 'position.R'

who use functions like Filter or Reduce in their code, as well
as adding new functions such as Zipwith and Partition to the
Collate: 'call_mclapply.R' 'filter.R' 'mchof.R' 'partition.R'

Description: mchof (read: mac'hoff) is a utility library that provides
composable, consistent parallel implementations of functionals
such as Filter, Fold, ZipWith and Partition.
Suggests: testthat, itertools, microbenchmark, ggplot2, reshape2
Collate: 'call_mclapply.R' 'filter.R' 'partition.R' 'position.R'
'reduce.R' 'unzip.R' 'zip.R' 'quantify.R' 'reject.R' 'fold.R'

Title: Estimating Conditional Auto-Regressive (CAR) Models using Monte
Authors@R: c(person("Zhe", "Sha", role = c("aut", "cre"), email =
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.
Imports: spam, rsm, fields, maxLik, nleqslv, spdep

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) <doi:10.1016/j.spasta.2017.01.002>.