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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

VersionRepositoryFileSize
1.1.2 rolling linux/jammy R-4.5 clespr_1.1.2.tar.gz 76.0 KiB
1.1.2 rolling linux/noble R-4.5 clespr_1.1.2.tar.gz 76.0 KiB
1.1.2 rolling source/ R- clespr_1.1.2.tar.gz 14.2 KiB
1.1.2 latest linux/jammy R-4.5 clespr_1.1.2.tar.gz 76.0 KiB
1.1.2 latest linux/noble R-4.5 clespr_1.1.2.tar.gz 76.0 KiB
1.1.2 latest source/ R- clespr_1.1.2.tar.gz 14.2 KiB
1.1.2 2026-04-26 source/ R- clespr_1.1.2.tar.gz 14.2 KiB
1.1.2 2026-04-23 source/ R- clespr_1.1.2.tar.gz 14.2 KiB
1.1.2 2026-04-09 windows/windows R-4.5 clespr_1.1.2.zip 78.5 KiB
1.1.2 2025-04-20 source/ R- clespr_1.1.2.tar.gz 14.2 KiB

Dependencies (latest)

Imports