Package: remotePARTS 1.0.4

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.

Authors:Clay Morrow [aut, cre], Anthony Ives [aut]

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remotePARTS.pdf |remotePARTS.html✨
remotePARTS/json (API)
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Peer review:

Bug tracker:https://github.com/morrowcj/remoteparts/issues

On CRAN:

autocorrelationbig-dataremote-sensing-in-rstatistical-analysis

20 exports 20 stars 2.15 score 10 dependencies 9 scripts 211 downloads

Last updated 1 years agofrom:476c8d4abe. Checks:OK: 9. Indexed: yes.

Exports:check_posdefchisqrcovar_expcovar_exppowcovar_taperdistm_kmdistm_scaledfitARfitAR_mapfitCLSfitCLS_mapfitCorfitGLSfitGLS_optfitGLS_partitioninvert_cholmulticore_fitGLS_partitionpart_csvpart_datasample_partitions

Dependencies:codetoolsCompQuadFormdoParallelforeachgeosphereiteratorslatticeRcppRcppEigensp