* function

  - changed function `SESraster()` to allow directly suplying of function with randomization algorithm; also improved method of supplying arguments through FUN_args and alg_args
  - updated `algorithm_metrics()`
  - added `plot_alg_metrics()` to plot site and species difference metrics
 between original and randomized rasters

DESCRIPTION

@@ -13,16 +13,21 @@ Description: Randomization of presence/absence species distribution raster
     data with or without including spatial structure for calculating
     standardized effect sizes and testing null hypothesis. The
     randomization algorithms are based on classical algorithms for
-    matrices (Gotelli 2000, <doi:10.2307/177478>) implemented for
-    raster data.
+    matrices (Gotelli 2000, <doi:10.2307/177478>) implemented for raster
+    data.
 License: GPL (>= 3)
 URL: https://github.com/HemingNM/SESraster,
     https://hemingnm.github.io/SESraster/
 BugReports: https://github.com/HemingNM/SESraster/issues
 Depends: 
     R (>= 2.10)
 Imports: 
-    terra
+    graphics,
+    methods,
+    rlang,
+    stats,
+    terra,
+    utils
 Suggests: 
     kableExtra,
     knitr,

NAMESPACE

@@ -1,10 +1,11 @@
 # Generated by roxygen2: do not edit by hand
 
 export(SESraster)
-export(algorithm_performance)
+export(algorithm_metrics)
 export(bootspat_ff)
 export(bootspat_naive)
 export(bootspat_str)
 export(fit.memory)
 export(fr2prob)
 export(load_ext_data)
+export(plot_alg_metrics)

NEWS.md

@@ -3,10 +3,11 @@
 * bug fixes
   - bug fix on attachMSG
 
-* new function
+* new functions
   - added `SESraster()` to handle aleatorizations
-  - added `algorithm_performance()` to compare randomized rasters with observed
-  rasters
+  - added `algorithm_metrics()` to compare original and randomized rasters
+  - added `plot_alg_metrics()` to plot site and species difference metrics 
+  between original and randomized rasters
 
 * function changes
   - changed function name from .fit.memory() to fit.memory() and added argument "n"

R/bootspat_ff.R

@@ -31,7 +31,7 @@
 #' @inheritParams terra::app
 #' @param ... additional parameters for terra::app
 #' @seealso \code{\link{bootspat_str}}, \code{\link{bootspat_naive}},
-#' \code{\link{SESraster}}, \code{\link{algorithm_performance}}
+#' \code{\link{SESraster}}, \code{\link{algorithm_metrics}}
 #' @author Neander Marcel Heming
 #'
 #' @references Connor, E. F., & Simberloff, D. (1979). The Assembly of Species Communities: Chance or Competition? Ecology, 60(6), 1132–1140.

R/bootspat_naive.R

@@ -45,7 +45,7 @@
 #' @param ... additional arguments to be passed passed down from a calling function.
 #' @return SpatRaster object
 #' @seealso \code{\link{bootspat_str}}, \code{\link{bootspat_ff}},
-#' \code{\link{SESraster}}, \code{\link{algorithm_performance}}
+#' \code{\link{SESraster}}, \code{\link{algorithm_metrics}}
 #' @author Neander Marcel Heming and Gabriela Alves-Ferreira
 #'
 #' @examples

R/bootspat_str.R

@@ -100,7 +100,7 @@ fr2prob <- function(x, rprob=NULL){
 #' @inheritParams terra::app
 #' @param ... additional parameters for terra::app
 #' @seealso \code{\link{bootspat_naive}}, \code{\link{bootspat_ff}},
-#' \code{\link{SESraster}}, \code{\link{algorithm_performance}}
+#' \code{\link{SESraster}}, \code{\link{algorithm_metrics}}
 #'
 #' @author Neander Marcel Heming
 #'

R/memory_check.R

@@ -11,6 +11,8 @@ fit.memory <- function(x, n=1){
   # x rasters will be generated in this function, let's see if there is enough memory in the user's pc
   sink(nullfile())    # suppress output
   mi <- terra::mem_info(x, n)[5] != 0 # proc in memory = T TRUE means that it fits in the pc's memory, so you wouldn't have to use temporary files
-  sink()
+  names(mi) <- NULL
+  sink(file = NULL)
+  # sink(file = NULL)
   return(mi)
 }

README.md

@@ -9,28 +9,33 @@
 # SESraster <a href="https://hemingnm.github.io/SESraster/"><img src="man/figures/logo.png" align="right" height="139" alt="SESraster website" /></a>
 
 Randomization of presence/absence species distribution raster data with or without including spatial structure for calculating standardized effect sizes and testing null hypothesis.
-The spatially unstructured randomization algorithms are based on classical algorithms for matrices (Gotelli 2000, <doi:10.2307/177478>) but implemented for raster data.
-
+The randomization algorithms are based on classical algorithms for matrices (Gotelli 2000, <doi:10.2307/177478>) implemented for raster data.  
+  
 ### Installation
-To install the package, run the following code:
+To install the package, run:
 ```
 install.packages("SESraster")
 ```
-The development version of `SESraster` can be installed from the [`SESraster repository`](https://github.com/HemingNM/SESraster) in Github:
+The development version can be installed from the [`SESraster repository`](https://github.com/HemingNM/SESraster) in Github:
 ```
 require(devtools)
-devtools::load_all()
-devtools::install_github("HemingNM/SESraster", build_vignettes = TRUE)
+load_all()
+install_github("HemingNM/SESraster", build_vignettes = TRUE)
 library(SESraster)
 ```
 
-
 ### Examples
 Vignettes can be found at the [package's webpage](https://hemingnm.github.io/SESraster/) or loading:
 ```
 browseVignettes("SESraster")
 ```
 
+### Citation
+If this package is useful to you, please cite it in your publications. 
+Find more information using:
+```
+citation("SESraster")
+```
 
 ### Issues
 If you have any question or find any bug, let us know through the topic ["Issues"](https://github.com/HemingNM/SESraster/issues).