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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