Dear CRAN maintainers,
This is a minor update to the spatialwarnings package with minor improvements, along with a few bug fixes.
This release should fix the problem with leftovers 'assert()s' raised by Prof. Brian Ripley. Thanks for pointing those out, I thought they were stripped from CRAN package builds.
Thanks,
Alexandre Génin
This package was tested using the following environments:
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R-lib Github actions (macOS with R-latest, windows with R-latest, ubuntu-latest with R-devel, ubuntu-latest with R-release, ubuntu-latest with R-oldrel): https://travis-ci.org/spatial-ews/spatialwarnings
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Local linux computer (Arch Linux as of 2021-03-10, R 4.1.2)
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M1 Mac using https://mac.r-project.org/macbuilder/submit.html (results at: https://mac.r-project.org/macbuilder/results/1647804133-ff347830befb7607/)
One remaining NOTE occurred on some platforms:
The package size is sometimes reported as exceeding 1Mb (on ubuntu platforms above), probably due to the use of Rcpp:
- checking installed package size ... NOTE installed size is 7.9Mb sub-directories of 1Mb or more: libs 6.6Mb
This release has received external contributions from J. Guerber.
Improvements:
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In patch size distribution plots, fits are now rescaled when xmin is above 1 so they overlay nicely on the observed distribution
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Better handling of errors in segmented::segmented() when fitting variograms
Bug fixes:
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Fixed an error occurring when using xmin = "estimate" in patchdistr_sews
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Remove calls to assert() that were leftover from testing (CRAN policy)
spatialwarnings is a package that assists ecologists in carrying out computations of early-warning signals (EWS) of ecosystem degradation.
These EWS are based on the fact that some ecosystems are expected to show specific spatial patterns before undergoing non-linear transitions (a wide shift in their state despite a small change in external forcings). For example, such ecosystems are expected to show an increase in spatial autocorrelation, variance and skewness, or, for patchy ecosystems, specific changes in the patch size distribution.
This packages assists users with computing these metrics efficiently on matrix objects in R, test their significance based on randomizing spatial structure, and plot their trends based on ggplot2. A convenient, three-step workflow is provided based on summary/plot/etc. generic functions.
Homepage and usage example:
https://github.com/spatial-ews/spatialwarnings
Reference:
- Génin, A. , Majumder, S. , Sankaran, S. , Danet, A. , Guttal, V. , Schneider, F. D. and Kéfi, S. (2018), Monitoring ecosystem degradation using spatial data and the R package 'spatialwarnings'. Methods Ecol Evol. doi:10.1111/2041-210X.13058