spmoran
Fast Spatial and Spatio-Temporal Regression using Moran Eigenvectors
A collection of functions for estimating spatial and spatio-temporal regression models. Moran eigenvectors are used as spatial basis functions to efficiently approximate spatially dependent Gaussian processes (i.e., random effects eigenvector spatial filtering; see Murakami and Griffith 2015 doi:10.1007/s10109-015-0213-7). The implemented models include linear regression with residual spatial dependence, spatially/spatio-temporally varying coefficient models (Murakami et al., 2017, 2024; doi:10.1016/j.spasta.2016.12.001, doi:10.48550/arXiv.2410.07229), spatially filtered unconditional quantile regression (Murakami and Seya, 2019 doi:10.1002/env.2556), Gaussian and non-Gaussian spatial mixed models through compositionally-warping (Murakami et al. 2021, doi:10.1016/j.spasta.2021.100520).
- Version0.3.1
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release10/12/2024
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Team
Daisuke Murakami
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- Imports10 packages
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