Crandore Hub

varycoef

Modeling Spatially Varying Coefficients

Implements a maximum likelihood estimation (MLE) method for estimation and prediction of Gaussian process-based spatially varying coefficient (SVC) models (Dambon et al. (2021a) <doi:10.1016/j.spasta.2020.100470>). Covariance tapering (Furrer et al. (2006) <doi:10.1198/106186006X132178>) can be applied such that the method scales to large data. Further, it implements a joint variable selection of the fixed and random effects (Dambon et al. (2021b) <doi:10.1080/13658816.2022.2097684>). The package and its capabilities are described in (Dambon et al. (2021c) <doi:10.48550/arXiv.2106.02364>).

Versions across snapshots

VersionRepositoryFileSize
0.3.6 rolling linux/jammy R-4.5 varycoef_0.3.6.tar.gz 1.4 MiB
0.3.6 rolling linux/noble R-4.5 varycoef_0.3.6.tar.gz 1.4 MiB
0.3.6 rolling source/ R- varycoef_0.3.6.tar.gz 1.1 MiB
0.3.6 latest linux/jammy R-4.5 varycoef_0.3.6.tar.gz 1.4 MiB
0.3.6 latest linux/noble R-4.5 varycoef_0.3.6.tar.gz 1.4 MiB
0.3.6 latest source/ R- varycoef_0.3.6.tar.gz 1.1 MiB
0.3.6 2026-04-26 source/ R- varycoef_0.3.6.tar.gz 1.1 MiB
0.3.6 2026-04-23 source/ R- varycoef_0.3.6.tar.gz 1.1 MiB
0.3.6 2026-04-09 windows/windows R-4.5 varycoef_0.3.6.zip 1.4 MiB
0.3.5 2025-04-20 source/ R- varycoef_0.3.5.tar.gz 1.1 MiB

Dependencies (latest)

Imports

Suggests