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README.md

@@ -1,11 +1,11 @@
-[![CRAN version](http://www.r-pkg.org/badges/version/ENMeval)](https://CRAN.R-project.org/package=ENMeval) [![downloads](http://cranlogs.r-pkg.org/badges/grand-total/ENMeval?color=orange)](http://cranlogs.r-pkg.org/badges/grand-total/ENMeval?color=orange)
+[![CRAN version](https://www.r-pkg.org/badges/version/ENMeval)](https://CRAN.R-project.org/package=ENMeval) [![downloads](https://cranlogs.r-pkg.org:443/badges/grand-total/ENMeval?color=orange)](https://cranlogs.r-pkg.org:443/badges/grand-total/ENMeval?color=orange)
 
 # ENMeval
 R package for automated runs and evaluations of ecological niche models.
 
-[`ENMeval`](https://cran.r-project.org/package=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)](https://onlinelibrary.wiley.com/doi/full/10.1111/ecog.03049) using the ['maxnet' R package](https://cran.r-project.org/package=maxnet) 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:
+[`ENMeval`](https://cran.r-project.org/package=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)](https://onlinelibrary.wiley.com/doi/full/10.1111/ecog.03049) using the ['maxnet' R package](https://cran.r-project.org/package=maxnet) or [the original java program (https://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.](http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12261/full)
+[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.](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.12261)
 
 Also see the vignette for examples of implementation.
 

man/ENMeval-package.Rd

@@ -32,7 +32,7 @@ There are some differences between the 'maxnet' and 'maxent.jar' algorithms that
 \note{
 Currently, \pkg{ENMeval} only implements the Maxent algorithm (via either the 'maxent.jar' or 'maxnet' implementations), but we eventually plan to expand it to work with other algorithms.  All calculations are based on the raw Maxent output (i.e., \emph{not} logistic or cumulative transformations) and users can choose whether to use 'clamping' (see Maxent documentation for details on this option).  Additionally, Maxent models are run with the arguments: \code{noaddsamplestobackground} and \code{noremoveDuplicates}.  Users should consult Maxent documentation (Phillips \emph{et al.} 2006) and other references (e.g., Phillips and Dudik 2008) for more information on these options.  We note that interested users can edit the source code of \code{ENMeval} (in particular, the \code{\link{make.args}} and \code{\link{tuning}} functions) if they desire to change these or other options.
 
-When using the 'maxent.jar' implementation (\bold{\emph{not default as of version 0.3.0}}), \code{ENMevaluate} directly uses several functions from the \pkg{dismo} package (Hijmans \emph{et al.} 2011).  Most importantly, the \code{maxent} function that runs the Maxent algorithm (Phillips \emph{et al.} 2006) in Java.  Before running this command, the user must first download Maxent from \href{http://www.cs.princeton.edu/~schapire/maxent/}{this website}.  Then, place the file 'maxent.jar' in the 'java' folder of the \pkg{dismo} package. The user can locate that folder by typing: \code{system.file("java", package="dismo")}.  For additional details, users should consult the documentation of the \pkg{dismo} package (or just use the newer [default] \pkg{maxnet} implementation).
+When using the 'maxent.jar' implementation (\bold{\emph{not default as of version 0.3.0}}), \code{ENMevaluate} directly uses several functions from the \pkg{dismo} package (Hijmans \emph{et al.} 2011).  Most importantly, the \code{maxent} function that runs the Maxent algorithm (Phillips \emph{et al.} 2006) in Java.  Before running this command, the user must first download Maxent from \href{https://www.cs.princeton.edu/~schapire/maxent/}{this website}.  Then, place the file 'maxent.jar' in the 'java' folder of the \pkg{dismo} package. The user can locate that folder by typing: \code{system.file("java", package="dismo")}.  For additional details, users should consult the documentation of the \pkg{dismo} package (or just use the newer [default] \pkg{maxnet} implementation).
 
 }
 

man/make.args.Rd

@@ -21,7 +21,7 @@ When \code{labels = FALSE}, the following additional arguments are added:
 
 \code{noaddsamplestobackground, noremoveDuplicates, noautofeature}.
 
-For details on these arguments, see Phillips \emph{et al.} (2006) and the help documentation and tutorial of the Maxent software and the tutorial that can be downloaded from \href{http://www.cs.princeton.edu/~schapire/maxent/}{this website}.
+For details on these arguments, see Phillips \emph{et al.} (2006) and the help documentation and tutorial of the Maxent software and the tutorial that can be downloaded from \href{https://www.cs.princeton.edu/~schapire/maxent/}{this website}.
 }
 
 \value{

man/var.importance.Rd

@@ -33,7 +33,7 @@ var.importance(mod)
 
 \note{Both metrics should be interpreted with caution when the predictor variables are correlated (Phillips 2006).}
 
-\references{Phillips, S. (2006) A brief tutorial on Maxent. AT&T Research. Available at: http://www.cs.princeton.edu/~schapire/maxent/tutorial/tutorial.doc}
+\references{Phillips, S. (2006) A brief tutorial on Maxent. AT&T Research. Available at: https://www.cs.princeton.edu/~schapire/maxent/tutorial/tutorial.doc}
 
 \author{Jamie M. Kass <jkass@gc.cuny.edu> and Robert Muscarella <bob.muscarella@gmail.com>}