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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.1.2 |
2026-04-09 windows/windows R-4.5 | clespr_1.1.2.zip |
78.5 KiB |