Automated Runs and Evaluations of Ecological Niche Models
Automatically partitions data into evaluation bins, executes ecological niche models across a range of settings, and calculates a variety of evaluation statistics. Current version only implements ENMs with Maxent (Phillips et al. 2006) or maxnet (Phillips et al. 2017).
R package for automated runs and evaluations of ecological niche models.
ENMeval is an R package that performs automated runs and evaluations of ecological niche models, and currently implements Maxent using the either (now by default) the 'maxnet' algoritm developed by Phillips et al. (2017) using the 'maxnet' R package or [the original java program (http://biodiversityinformatics.amnh.org/open_source/maxent/).
ENMeval was made for those who want to "tune" their models to maximize predictive ability and avoid overfitting, or in other words, optimize model complexity to balance goodness-of-fit and predictive ability. The primary function,
ENMevaluate, does all the heavy lifting and returns several items including a table of evaluation statistics and, for each setting combination, a model object and a raster layer showing the model prediction across the study extent. There are also options for calculating niche overlap between predictions, running in parallel to speed up computation, and more. For a more detailed description of the package, check out the open-access publication:
Muscarella, R., Galante, P. J., Soley-Guardia, M., Boria, R. A., Kass, J. M., Uriarte, M. and Anderson, R. P. (2014), ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in Ecology and Evolution, 5: 1198–1205.
Also see the vignette for examples of implementation.
Note that as of version 0.3.0, the default implementation uses the 'maxnet' R package. The output from this differs from that of the original java program and so some features are not compatible (e.g., variable importance, the old html output). Our team has done some fairly extensive testing to ensure this implementation gives the expected results but the maxnet implementation is relatively new (at the time of writing this) and we encourage users to scrutinize their results.
Functions in ENMeval
|get.evaluation.bins||Methods to partition data for evaluation|
|calc.aicc||Calculate AICc from Maxent model prediction|
|var.importance||Extract percent contribution and permutation importance from a Maxent model|
|enmeval_results||An object of class "ENMevaluation"|
|eval.plot||Generate Basic Plot for ENMevaluate Output|
|calc.niche.overlap||Calculate Similarity of ENMs in Geographic Space|
|corrected.var||Calculate variance corrected for non-independence of k-fold iterations|
|ENMevaluate||Tuning and evaluation of ENMs with Maxent|
|ENMeval-package||Automated runs and evaluations of ecological niche models|
|make.args||Generate arguments for Maxent|
|maxnet.functions||Functions for compatability with maxnet package|
|eval2||An object of class "ENMevaluation"|
Vignettes of ENMeval
Last month downloads
|Packaged||2018-08-14 19:10:12 UTC; au529793|
|Date/Publication||2018-08-15 08:10:03 UTC|
|depends||dismo , methods , R (>= 3.4)|
|imports||doParallel , foreach , graphics , grDevices , maxnet , raster , stats , utils|
|suggests||knitr , maptools , parallel , rgeos , rJava (>= 0.5-0) , rmarkdown , sp , spocc|
|Contributors||Maria Uriarte, Robert Boria, Robert Anderson, Robert Muscarella, Peter Galante, Mariano Soley-Guardia, Jamie Kass|
Include our badge in your README