INDperform is an R package for validating the performance of ecological state indicators and assessing the ecological status based on a suite of indicators.
Install the development version from GitHub using ‘remotes’:
# install.packages("remotes") remotes::install_github("saskiaotto/INDperform")
If you encounter a clear bug, please file a minimal reproducible example on github. For questions email me any time.
For more information, documentation and examples of use, please see INDperform website at https://saskiaotto.github.io/INDperform/
For guidance on how to apply the functions step-by-step see also the INDperform cheatsheet. We are currently working on the Vignette but if you want more information on the framework for quantifying IND performances and its statistical tools implemented in this package see
Otto, S.A., Kadin, M., Casini, M., Torres, M.A., Blenckner, T. (2018): A quantitative framework for selecting and validating food web indicators. Ecological Indicators, 84: 619-631, doi: https://doi.org/10.1016/j.ecolind.2017.05.045
In Version 0.2.2 some adjustments were made to account for changes in packages INDperform depends on and a minor bug fixed in the NRMSE model prediction plot. Some of the plotting functions include now also titles in the individual panels. A data validation routine was added to check for unwanted characters in indicator or pressure names which caused models to not build. For more details see the NEWS file.
In Version 0.2.1 a minor bug with different internal test results under different R versions was fixed by modifying some tests. But this bug did not affect the modelling results or performance of the previous version.
Version 0.2.0 has been released on CRAN 2019-02-10! The new version
includes a few internal changes as adjustments to updated packages it
depends on. Major changes changes include a new NRMSE calculation based
on the standard deviation and back-transformation (see
for the motivation), an NRMSE stand-alone function (
nrmse()) and a
function that allows the calculation of the distance matrix averaged
across groups (i.e. a weighted distance matrix) (
more information see the
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