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remotePARTS  

Spatiotemporal Autoregression Analyses for Large Data Sets
View on CRAN: Click here


Download and install remotePARTS package within the R console
Install from CRAN:
install.packages("remotePARTS")

Install from Github:
library("remotes")
install_github("cran/remotePARTS")

Install by package version:
library("remotes")
install_version("remotePARTS", "1.0.4")



Attach the package and use:
library("remotePARTS")
Maintained by
Clay Morrow
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-09-15
Latest Update: 2023-09-15
Description:
These tools were created to test map-scale hypotheses about trends in large remotely sensed data sets but any data with spatial and temporal variation can be analyzed. Tests are conducted using the PARTS method for analyzing spatially autocorrelated time series (Ives et al., 2021: <doi:10.1016/j.rse.2021.112678>). The method's unique approach can handle extremely large data sets that other spatiotemporal models cannot, while still appropriately accounting for spatial and temporal autocorrelation. This is done by partitioning the data into smaller chunks, analyzing chunks separately and then combining the separate analyses into a single, correlated test of the map-scale hypotheses.
How to cite:
Clay Morrow (2023). remotePARTS: Spatiotemporal Autoregression Analyses for Large Data Sets. R package version 1.0.4, https://cran.r-project.org/web/packages/remotePARTS
Previous versions and publish date:
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