clespr: Composite Likelihood Estimation for Spatial Data

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.

Version: 1.1.2
Depends: R (≥ 3.2.0)
Imports: AER (≥ 1.2-5), pbivnorm (≥ 0.6.0), MASS (≥ 7.3-45), magic (≥ 1.5-6), survival (≥ 2.37-5), clordr (≥ 1.0.2), doParallel (≥ 1.0.11), foreach (≥ 1.2.0), utils, stats
Published: 2018-02-23
Author: Ting Fung (Ralph) Ma [cre, aut], Wenbo Wu [aut], Jun Zhu [aut], Xiaoping Feng [aut], Daniel Walsh [ctb], Robin Russell [ctb]
Maintainer: Ting Fung (Ralph) Ma <tingfung.ma at wisc.edu>
License: GPL-2
NeedsCompilation: no
CRAN checks: clespr results

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Reference manual: clespr.pdf
Package source: clespr_1.1.2.tar.gz
Windows binaries: r-devel: clespr_1.1.2.zip, r-devel-gcc8: clespr_1.1.2.zip, r-release: clespr_1.1.2.zip, r-oldrel: clespr_1.1.2.zip
OS X binaries: r-release: clespr_1.1.2.tgz, r-oldrel: clespr_1.1.2.tgz
Old sources: clespr archive

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