ENMeval v2.0.0

o Complete redesign of nearly all functionality. Code is now much tidier and readable thanks to dplyr and tidyr, and commented documentation is more prevalent throughout.
o New object-oriented algorithm specification for using any algorithm with ENMeval. Implementations of the new ENMdetails object exist for maxent.jar, maxnet, and BIOCLIM as examples of what is possible.
o Automated output metadata for tuning analyses which uses the Range Model Metadata Standards (R package rangeModelMetadata).
o New function for running null ENM simulations and calculating significance and effect sizes for empirical model performance metrics (Bohl et al. 2019 -- check ?ENMnulls).
o New partition schema for fully withheld testing data and evaluation without partitions. Also includes a new option for spatial block partitions to customize the spatial orientation of the blocks.
o Now implements continuous Boyce Index for training, validation, and full withheld testing data (via R package ecospat), and allows use of custom evaluation functions with the user.eval argument (see ?ENMevaluate).
o A suite of new visualization functions using ggplot2 that map partition groups, plot environmental similarity histograms for partition groups, map environmental similarity for partition groups, and plot histograms or violins for null ENM results. The original evaluation plots were also redone and now use ggplot.
o New analysis options for more flexibility (see ?ENMevaluate).
o Fully updated and extensive vignette (https://jamiemkass.github.io/ENMeval/articles/ENMeval-2.0.0-vignette.html) that walks through a full analysis while describing all the new functionality.
o Now supports doSNOW parallelization as well as doParallel. The doSNOW option has a functioning progress bar for parallel processes.

ENMeval v0.3.1

o Changed the default behavior to use the 'maxnet' function of the 'maxnet' package instead of the 'maxent.jar' program, and removed the dependency on the 'rJava' package by default, among others.
o Added an algorithm slot to the ENMevaluation object.
o Added information on the aggregation factor(s) or number of k folds to the ENMevaluation object when relevant.
o Corrected use of 'corrected.var' in the 'tuning' function.
o Changed some column names in the @results table to be more intuitive and compatable with the R package, Wallace.
o Fixed broken URLs and now loading example data directly from RDS to avoid using GBIF (which is down sometimes).

ENMeval 0.2.2

Major changes in this version are:

o Added a vignette. After installing version 0.2.2, in R type:
vignette('ENMeval-vignette', package='ENMeval').
You can also view the vignette online here
o Added an option to pass additional arguments to maxent (e.g., prevalence)
o Added a function to extract variable importance for maxent models
o Fixed an inconsistency with the AICc columns of the results table when rasterPreds=F
o Fixed potential errors in the partitioning methods functions when used independently

Version 0.2.0

Additional significant changes include:

• Now activate parallel processing by setting parallel=TRUE, and change number of cores used with numCores.
• Added a "models" slot in ENMevaluation object class to hold Maxent model objects. This allows the user to access the lambda values and original results table generated by Maxent, and use the dismo::predict() function to create logistic predictions and project the model to new areas and/or time periods.
• Fixed a bug that allowed only a single categorical variable; now multiple categorical variables work.
• Added an argument in the ENMevaluate function to turn off raster prediction generation to save time (default is rasterPreds=TRUE).