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.

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Package details

AuthorTing Fung (Ralph) Ma [cre, aut], Wenbo Wu [aut], Jun Zhu [aut], Xiaoping Feng [aut], Daniel Walsh [ctb], Robin Russell [ctb]
MaintainerTing Fung (Ralph) Ma <[email protected]>
Package repositoryView on CRAN
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clespr documentation built on Feb. 24, 2018, 1:03 a.m.