remotePARTS: Spatiotemporal autoregression analyses for large data sets

Remote sensing produces enormous quantities of detailed data owing to the high spatial and temporal resolution provided by modern technologies.Data generated by these means can provide unparalleled insight into trends across the landscapes and time periods they represent.Data collected across space and through time, however, exhibit autocorrelation (the tendency of nearby observations in space and time to be more similar than distant ones) that needs to be accounted for during analyses.The size of remote-sensing data sets poses a substantive computational challenge as regression-type analyses that are designed to test hypotheses and account for autocorrelation struggle to fit large data sets (e.g., millions of pixels).