remotePARTS: Spatiotemporal Autoregression Analyses for Large Data Sets

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.

Version: 1.0.4
Depends: R (≥ 4.0)
Imports: stats, geosphere (≥ 1.5.10), Rcpp (≥ 1.0.5), CompQuadForm, foreach, parallel, iterators, doParallel
LinkingTo: Rcpp, RcppEigen
Suggests: dplyr (≥ 1.0.0), data.table, knitr, rmarkdown, markdown, sqldf, devtools, ggplot2, reshape2, sf
Published: 2023-09-15
Author: Clay Morrow ORCID iD [aut, cre], Anthony Ives ORCID iD [aut]
Maintainer: Clay Morrow <morrowcj at>
License: GPL (≥ 3)
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: remotePARTS results


Reference manual: remotePARTS.pdf
Vignettes: Alaska


Package source: remotePARTS_1.0.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): remotePARTS_1.0.4.tgz, r-oldrel (arm64): remotePARTS_1.0.4.tgz, r-release (x86_64): remotePARTS_1.0.4.tgz, r-oldrel (x86_64): remotePARTS_1.0.4.tgz


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