spmoran

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).

Last 30 days

This package has been downloaded 666 times in the last 30 days. This could be a paper that people cite without reading. Reaching the medium popularity echelon is no small feat! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 11 times.

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Last 365 days

This package has been downloaded 7,379 times in the last 365 days. A solid achievement! Enough downloads to get noticed at department meetings. The day with the most downloads was Sep 26, 2024 with 113 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.