Spatiotemporal Autoregression Analyses for Large Data Sets R package remotePARTS version 1.0.4 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. Software 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 Clay Morrow <morrowcj@outlook.com> Comprehensive R Archive Network (CRAN) Clay Morrow [aut, cre] (ORCID: <https://orcid.org/0000-0002-3069-3296>), Anthony Ives [aut] (ORCID: <https://orcid.org/0000-0001-9375-9523>) GPL (>= 3) 2023-09-15 application/tgz https://CRAN.R-project.org/package=remotePARTS doi:10.32614/CRAN.package.remotePARTS