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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:
install.packages("remotePARTS")To install the latest development version of this package from github, use
install.packages("devtools") # ensure you have the latest devtools
devtools::install_github("morrowcj/remotePARTS")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.
For examples on how to use remotePARTS, see the
Alaska vignette:
vignette("Alaska")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](https://support.posit.co/hc/en-us/articles/200486498-Package-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: https://morrowcj.github.io/remotePARTS/Alaska.html.
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. https://doi.org/10.1016/j.rse.2021.112678
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.