Title: Simultaneous Variables Clustering and Regression
Description: 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).
Collate: 'fitClere.R' 'Clere.R' 'sClere-Class.R' 'fitPacs.R'

Description: 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).

Description: 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>).
Depends: R (>= 2.10), parallel, methods, lasso2

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

Description: 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
Imports: Rcpp (>= 1.0.0), graphics, methods, parallel, utils
Collate: 'algoComp.R' 'numExpRealData.R' 'numExpSimData.R' 'fitClere.R'

Title: Composite Likelihood Estimation for Spatial Data
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"),
Description: 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.