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 DOI: 10.32614/CRAN.package.remotePARTS Author: Clay Morrow [aut, cre], Anthony Ives [aut] Maintainer: Clay Morrow <morrowcj at outlook.com> BugReports: https://github.com/morrowcj/remotePARTS/issues License: GPL (≥ 3) URL: https://github.com/morrowcj/remotePARTS NeedsCompilation: yes Materials: README NEWS CRAN checks: remotePARTS results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=remotePARTS to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4