Simultaneous Variables Clustering and Regression
The R package clere implements an empirical Bayes approach for simultaneous variable clustering and regression. This version also (re)implements in C++ an R script proposed by Howard Bondell that fits the Pairwise Absolute Clustering and Sparsity (PACS) methodology (see Sharma DB et al. - 2013).

Implements an empirical Bayes approach for simultaneous variable clustering and regression. This version also (re)implements in C++ an R script proposed by Howard Bondell that fits the Pairwise Absolute Clustering and Sparsity (PACS) methodology (see Sharma DB et al. - 2013).

Implements an empirical Bayes approach for simultaneous variable clustering and regression. This version also (re)implements in C++ an R script proposed by Howard Bondell that fits the Pairwise Absolute Clustering and Sparsity (PACS) methodology (see Sharma et al (2013) <DOI:10.1080/15533174.2012.707849>).

CLERE methodology for simultaneous variables clustering and
regression.
The R package clere implements an empirical Bayes approach for simultaneous variable clustering and regression.

c(person(given = "Loic",
family = "Yengo",
role = c("aut", "cre"),
email = "loic.yengo@gmail.com"),
person(given = "Mickaël",
family = "Canouil",
role = "aut",
email = "mickael.canouil@cnrs.fr",
comment = c(ORCID = "0000-0002-3396-4549")))
Implements an empirical Bayes approach for
simultaneous variable clustering and regression. This version also
(re)implements in C++ an R script proposed by Howard Bondell that fits
the Pairwise Absolute Clustering and Sparsity (PACS) methodology (see
Sharma et al (2013) <DOI:10.1080/15533174.2012.707849>).
Rcpp (>= 1.0.0), graphics, methods, parallel, utils
'algoComp.R' 'numExpRealData.R' 'numExpSimData.R' 'fitClere.R'
'Clere-class.R' 'sClere-class.R' 'fitPacs.R' 'Pacs-class.R'
'clere-package.R'
Loic Yengo [aut, cre],
Mickaël Canouil [aut] (<https://orcid.org/0000-0002-3396-4549>)

Composite Likelihood Estimation for Spatial Data
c(
person("Ting Fung (Ralph)", "Ma", email = "tingfung.ma@wisc.edu", role = c("cre", "aut")),
person("Wenbo", "Wu", email = "wenbowu@umich.edu", role = "aut"),
person("Jun", "Zhu", email = "jzhu@stat.wisc.edu", role = "aut"),
person("Xiaoping", "Feng", role = "aut"),
person("Daniel", "Walsh", role= "ctb"),
person("Robin", "Russell", role= "ctb"))
Composite likelihood approach is implemented to estimating statistical models for spatial ordinal and proportional data based on Feng et al. (2014) <doi:10.1002/env.2306>. Parameter estimates are identified by maximizing composite log-likelihood functions using the limited memory BFGS optimization algorithm with bounding constraints, while standard errors are obtained by estimating the Godambe information matrix.
Ting Fung (Ralph) Ma [cre, aut],
Wenbo Wu [aut],
Jun Zhu [aut],
Xiaoping Feng [aut],
Daniel Walsh [ctb],
Robin Russell [ctb]