License: GPL v3

Lifecycle: stable


remotePARTS is an R package that contains tools for analyzing spatiotemporal data, typically obtained via remote sensing.


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). 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.


To install the package and it’s dependencies, use the following R code:


To install the latest development version of this package from github, use

install.packages("devtools") # ensure you have the latest devtools

Then, upon successful installation, load the package with library(remotePARTS).

The latest version of Rtools is required for Windows and C++11 is required for other systems.

Example usage

For examples on how to use remotePARTS, see the Alaska vignette:


Note that the vignette needs to be built when installing with and may require the build_vignettes = TRUE argument when installing with install_github().

If you’re having trouble installing or building the package, you may need to double check that the R build tools are properly installed on your machine: official Rstudio development prerequisites]( To do this, use pkgbuild::has_build_tools(debug = TRUE) and pkgbuild::check_build_tools(debug = TRUE) to unsure that your build tools are up to date.

The vignette is also available online:

Bugs and feature requests

If you find any bugs, have a feature or improvement to suggest, or any other feedback about the remotePARTS package, please submit a GitHub Issue here. We really appreciate any and all feedback.


Ives, Anthony R., et al. “Statistical inference for trends in spatiotemporal data.” Remote Sensing of Environment 266 (2021): 112678.