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diff --git a/DESCRIPTION b/DESCRIPTION
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+Package: changeRangeR
+Type: Package
+Title: Change Metrics for Species Geographic Ranges
+Version: 1.0.0
+Authors@R: c(person('Peter J.', 'Galante',email='pgalante@amnh.org',role=c('aut')),
+    person('Samuel','Chang',email='changalfsam@gmail.com',role=c('aut')),
+    person('Andrea','Paz',email='paz.andreita@gmail.com',role=c('aut')),
+    person('Jamie M.','Kass',email='jamie.kass@oist.jp',role=c('aut')),
+    person('Matthew','Aiello-Lammens',email='maiellolammens@pace.edu',role=c('aut')),
+    person('Beth','Gerstner',email='gerstn11@msu.edu',role=c('aut')),
+    person('Bethany','Johnson',email='bjohnso005@citymail.cuny.edu',role=c('aut')),
+    person('Cory','Merow',email='cory.merow@gmail.com',role=c('aut')),
+    person('Elkin','Noguera-Urbano',email='enoguera@humboldt.org.co',role=c('aut')),
+    person('Gonzalo E.','Pinilla-Buitrago',email='gpinillabuitrago@gradcenter.cuny.edu',role=c('aut')),
+    person('Dan','Rosauer',email='dan.rosauer@anu.edu.au',role=c('aut')),
+    person('Mary E.','Blair',email='mblair1@amnh.org',role=c('aut','cre')))
+Description: Facilitates workflows to reproducibly transform estimates of species’ distributions into metrics relevant for conservation. For example, combining predictions from species distribution models with other maps of environmental data to characterize the proportion of a species’ range that is under protection, calculating metrics used under the IUCN Criteria A and B guidelines (Area of Occupancy and Extent of Occurrence), and calculating more general metrics such as taxonomic and phylogenetic diversity, as well as endemism. Also facilitates temporal comparisons among biodiversity metrics to inform efforts towards complementarity and consideration of future scenarios in conservation decisions. 'changeRangeR' also provides tools to determine the effects of modeling decisions through sensitivity tests.
+License: GPL-3
+VignetteBuilder: knitr
+Encoding: UTF-8
+Depends: R (>= 4.0)
+Imports: phylobase, raster, dplyr, rangeModelMetadata, rgeos, sf, sp
+Suggests: ape, rgdal, tidyverse, rmarkdown, testthat, tinytex, knitr,
+        picante, wallace, R.utils, tools, dismo
+RoxygenNote: 7.2.0
+NeedsCompilation: no
+Packaged: 2022-11-19 22:42:10 UTC; kass1
+Author: Peter J. Galante [aut],
+  Samuel Chang [aut],
+  Andrea Paz [aut],
+  Jamie M. Kass [aut],
+  Matthew Aiello-Lammens [aut],
+  Beth Gerstner [aut],
+  Bethany Johnson [aut],
+  Cory Merow [aut],
+  Elkin Noguera-Urbano [aut],
+  Gonzalo E. Pinilla-Buitrago [aut],
+  Dan Rosauer [aut],
+  Mary E. Blair [aut, cre]
+Maintainer: Mary E. Blair <mblair1@amnh.org>
+Repository: CRAN
+Date/Publication: 2022-11-21 11:00:02 UTC
diff --git a/MD5 b/MD5
@@ -0,0 +1,100 @@
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+031af8234ad76481645504ae0569ba78 *NAMESPACE
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+eb9bda58738c3e9477058e94cbfde8cb *vignettes/BiodivMetrics.Rmd
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diff --git a/NAMESPACE b/NAMESPACE
@@ -0,0 +1,13 @@
+# Generated by roxygen2: do not edit by hand
+
+export(AOOarea)
+export(SpeciesEndemism)
+export(buildRMM)
+export(calc_PE)
+export(complementarity)
+export(envChange)
+export(futureOverlap)
+export(mcp)
+export(mcpSDM)
+export(ratioOverlap)
+import(phylobase)
diff --git a/R/PD_from_rasters.R b/R/PD_from_rasters.R
@@ -0,0 +1,160 @@
+# ######Phylogenetic diversity from distribution models in tif format and phylogenies in nexus format######
+# ###This script applies to any distribution map in raster format changing only the extension type in line 52###
+# #############Last updated January 2014 by Andrea Paz#################
+#
+#
+# #Clean workspace
+# rm(list=ls())
+#
+# #Load required packages
+# library(maptools)
+# library(raster)
+# library(rgdal)
+# library(picante)
+# #####Function to load and crop rasters######
+#
+# funcion1=function (x, mask=selected_mask,factor=resolution) {
+#   capa=raster(x)
+#   capa1=aggregate(capa,factor,fun=max)
+#   capa2=crop(capa1,extent(mask))
+#   return(capa2)
+# }
+#
+# ########################################USER DEFINED VARIABLES#########################################
+# ######Prompt user to determine location of files and selected geographic areas, resolution etc.########
+# #######################################################################################################
+#
+#
+#   {print("Remember phylogeny file must be stored in working directory and named phylogeny.nex")
+#
+#   # set working directory
+#   print("Choose working directory for storage:")
+#   working_directory<-choose.dir()
+#   setwd(working_directory)
+#
+#   #Select folder containing distribution maps
+#   print("Select folder containing distribution maps")
+#   maps_folder<-choose.dir()
+#
+#   #File containing shape of the area to be analyzed (can be a country, province, ecosystem etc.). It will be treated as a mask
+#    print("Select working mask (i.e Colombia)")
+#    working_mask<-file.choose()
+#
+#   #Determine resolution as aggregation factor
+#   resolution1<-readline("Aggregation factor for model (Integer): ")
+#    resolution<-as.numeric(resolution1)
+#   #Determine names for outputs
+#   PD_map<-readline("Name of phylogenetic diversity raster: Do not use spaces and must include .asc extention (Ex: PD_country.asc): ")
+#   species_richness<-readline("Name of the species richness map: Do not use spaces and must include .asc extention  (Ex: Species_richness_country.asc): ")
+#
+#   #Read distribution files and obtain species names for phylogeny
+#
+#   distribution_files<-list.files(path=maps_folder, pattern= "*.tif$")
+#   species_names<-sub("_10p_cut.tif","",distribution_files)
+#   table1<-as.data.frame(species_names)
+#   colnames(table1)<-"Grid"
+#   write.table(table1,"species_list.txt",quote=F,row.names=F) #seria bueno que agregara el nombre de la mascara para saber si es colombia o paramo
+#
+#   #Read Shape of the geographic Mask
+#   selected_mask<-readShapePoly(working_mask)
+#
+# #########################
+# ####START ANALYSES#######
+# #########################
+#    setwd(maps_folder)
+# #Create empty raster of the desired area and resolution to assign pixel numbers
+# r<-raster(distribution_files[1])
+# r=aggregate(r,resolution,fun=max)
+# r<-crop(r,extent(selected_mask))
+# grid=r
+# names(grid)="grid"
+# grid[1:ncell(grid)]<-1:ncell(grid)
+# grid_list<-list(grid)
+# names(grid_list)<-"Grid"
+# #Load distribution files, aggregate and crop to Colombia extent
+#
+#    layers<-list()
+#    for (i in 1:length(distribution_files))
+#    {
+#     layers[i]<-funcion1(distribution_files[i])
+#     print(i)
+#    }
+# #layers<-lapply(distribution_files,funcion1)
+#
+#
+# #assign species names to layers with species distributions
+# names(layers)<-as.vector(table1$Grid)
+# layers[sapply(layers,is.null)]<-NULL
+#
+# #Combine distributions with pixel number raster
+# complete_list<-c(grid_list,layers)
+#
+# #Stack all distribution files and pixel numbers for PD computation
+# Stack<-stack(complete_list)
+#
+# #Convert to data frame for storage and posterior analysis
+#
+# community_data<-as.data.frame(Stack)
+#
+# #Store
+#    setwd(working_directory)
+# write.table(community_data, file = "communities.txt", append = FALSE,row.names=F,quote=F,sep="\t") #tratar de agregar nombre de mascara
+#
+#
+# #Generate a raster of taxonomic diversity (TD) for comparison
+# all_species<-stack(layers)
+# TD<-calc(all_species,sum)
+# writeRaster(TD,species_richness)
+# }
+#
+# #####This part when the phylogeny is ready#####
+#
+# #Phylogenetic diversity computation
+#
+# #Read phylogeny file in nexus format
+#   ##To read a phylogeny in newick format use read.tree instead of read.nexus
+#       #In working directory always a file with the same name: phylogeny.nex
+#
+#   user_phylogeny=read.nexus("phylogeny.nex")
+#
+#
+# #In the community data frame NA must be eliminated
+# community_data=na.omit(community_data)
+# head(community_data)
+#
+# #III-Phylogenetic diversity computation
+#   #computes only Faith�s PD others may be added
+#
+# pd.result <-pd(community_data[,2:ncol(community_data)],user_phylogeny,include.root = TRUE)
+#
+#
+# #Add the pixel PD value to data frame
+# community_data$pd<-pd.result[[1]]
+#
+# #Write the new matrix to a file to avoid rerunning the script for potential further analyses
+# write.table(community_data, file = "communities_and_pd.txt", append = FALSE,row.names=F,quote=F,sep="\t")
+#
+# #IV-Generate a raster containing PD information per pixel
+#
+# #1-First generate an empty raster using a base model for resolution and area
+#
+# r<-raster(ncol=1462,nrow=624)
+# res(r)<-resolution
+# r<-crop(r,extent(selected_mask))
+# values(r)<-0
+# map<-readOGR(dsn=maps_folder,layer=species_names[[1]])
+# r<-rasterize(map,r,map$PRESENCE,update=T)
+# r<-mask(r,selected_mask)
+# pd_ras<-r
+# values(pd_ras)<-NA #Eliminate every value they will be replaced by PD values further down
+#
+#
+# #2- Assign PD values to raster
+# pd_ras[community_data$Grid]<-community_data$pd
+#
+# #3- Save raster to file
+#
+# writeRaster(pd_ras,PD_map)
+#
+# #4-Optional plotting map in R (write to pdf?)
+# plot(pd_ras)
diff --git a/R/SE.R b/R/SE.R
@@ -0,0 +1,40 @@
+#' @name Species Endemism
+#' @title Calculate species endemism
+#' @description Calculate species endemism as the number of species in a place divided by the total number of places in which those species are found.
+#' @param rStack a rasterStack of binary species presences
+#' @return Raster object showing species endemism.
+#' @author pgalante@@amnh.org
+#' @examples
+#' # create binary raster
+#' r1 <- raster::raster(nrows=108, ncols=108, xmn=-50, xmx=50)
+#' raster::values(r1)<- runif(n = (108*108))
+#' r1[r1 < 0.5] <- NA
+#' r1[r1 > 0.5] <- 1
+#' r2 <- raster::raster(nrows=108, ncols=108, xmn=-50, xmx=50)
+#' raster::values(r2)<- runif(n = (108*108))
+#' r2[r2 < 0.5] <- NA
+#' r2[r2 > 0.5] <- 1
+#' r3 <- raster::raster(nrows=108, ncols=108, xmn=-50, xmx=50)
+#' raster::values(r3)<- runif(n = (108*108))
+#' r3[r3 < 0.5] <- NA
+#' r3[r3 > 0.5] <- 1
+#' rStack <- raster::stack(r1, r2, r3)
+#' # calculate SE
+#' SpeciesEndemism(rStack)
+#' @export
+
+
+SpeciesEndemism <- function (rStack){
+  #require(raster)
+  rStack[rStack == 0] <- NA
+  p.df <- as.data.frame(raster::rasterToPoints(rStack))
+  sspTotal <- raster::colSums(p.df, na.rm = T)
+  ssp.df <- t(p.df[-c(1, 2)]) * (sspTotal[-c(1, 2)])
+  ssp.PixSum <- raster::rowSums(t(ssp.df), na.rm = T)
+  spSum <- raster::rowSums(p.df[-c(1, 2)], na.rm = T)
+  SEvals <- spSum/ssp.PixSum
+  stackTotal <- sum(rStack, na.rm = T)
+  stackTotal[stackTotal == 0] <- NA
+  stackTotal[!is.na(stackTotal)] <- SEvals
+  return(stackTotal)
+}