update README

README.md

@@ -1,25 +1,27 @@
 # EFHSDM (in progress)
 
-A package is designed to produce SDMs and SDM visualizations as part of the 2022 EFH 5-year review. It is designed to be moderately flexible and can be expanded in the future, but for now assumes that abundance prediction is the end goal. 
+A package is designed to produce SDMs and SDM visualizations as part of the 2023 EFH 5-year review. It is designed to be moderately flexible and can be expanded in the future, but for now assumes that abundance prediction is the end goal. 
 
-The most recent version of this package was built in R 4.1.2.
+The most recent version of this package was built in R 4.4.1.
 
 Authors:
 
 @jeremyharris7  
 @James-Thorson-NOAA  
-@Ned-Laman-NOAA  
+@MasonSmith-NOAA
 @MargaretSiple-NOAA  
 @jodipirtle
-
+@Ned-Laman-NOAA  
 
 
 # Installation
 `EFHSDM` can be installed using the following code:
 ```r
 devtools::install_github("afsc-gap-products/akgfmaps", build_vignettes=TRUE)
-
 devtools::install_github("alaska-groundfish-efh/EFHSDM@main", dependencies = TRUE, build_vignettes = FALSE)
+
+# Development version - not currently recommended unless you are an active developer
+# devtools::install_github("alaska-groundfish-efh/EFHSDM@dev", dependencies = TRUE, build_vignettes = FALSE)
 ```
 
 
@@ -34,7 +36,7 @@ xtable, XML, raster, rgdal, gstat, sp, sf, stars, akgfmaps, ggplot, viridis, gri
 # Roadmap
 Sections of the analysis are included as separate scripts. The general strategy is to use the functions provided in `Functions_Maxent.R` and `Functions_GamModel.R` to produce models, abundance rasters, effects estimates, and other outputs in a standard-ish format. Then, the scripts `Functions_LoadMap.R` and `Functions_Ensemble.R` provide more general methods for mapping or plotting model outputs and combining inferences from multiple models.
 
-The `Meatgrinder.R` script provides an example of a workflow that combines these functions to make an ensemble SDM and the accompanying maps. It contains control logic designed to accommodate the needs of the 2022 EFH 5-year Review process, which involved running and keeping track of over 200 individual species/lifestages.
+The `Meatgrinder.R` script provides an example of a workflow that combines these functions to make an ensemble SDM and the accompanying maps. It contains control logic designed to accommodate the needs of the 2023 EFH 5-year Review process, which involved running and keeping track of over 200 individual species/lifestages.
 
 
 1) `Functions_Maxent.R` - this script provides the functions for quickly using maxnet models. 
@@ -97,9 +99,9 @@ The functions are typically called top to bottom. Begin by fitting a model using
 
 ## Simple example
 
-All of the rasters used for the EFH 2022 Five-Year Review are stored on a shared drive at NOAA. For the purposes of the following example, the datasets are embedded in the package. For additional rasters and datasets, contact the package developers or submit a data [product request](https://github.com/alaska-groundfish-efh/product-requests/issues).
+All of the rasters used for the EFH 2023 Five-Year Review are stored on a shared drive at NOAA. For the purposes of the following example, the datasets are embedded in the package. For additional rasters and datasets, contact the package developers or submit a data [product request](https://github.com/alaska-groundfish-efh/product-requests/issues).
 
-For example purposes, we will used only the last  years of data and only a few covariates. *Note that this means that the map you produce will look different from the final map produced in the 2022 EFH 5-year Review.*
+For example purposes, we will used only the last  years of data and only a few covariates. *Note that this means that the map you produce will look different from the final map produced in the 2023 EFH 5-year Review.*
 
 ### Load EFHSDM
 ```r
@@ -116,7 +118,8 @@ region.data$sponge <- as.integer(region.data$sponge > 0)
 region.data$logarea <- log(region.data$area)
 
 
-raster.stack <- raster_stack
+raster.stack <- terra::rast(terra::unwrap(raster_stack))
+#raster.stack <- raster::raster(raster.stack) # option to turn this spatraster stack into a regular raster stack (only works if you don't crop )
 ```
 
 ### Next we will fit a basic Poisson model and generate an abundance map