Remove all dependencies to maptools,rgeos,rgdal + indexation vignette

.Rbuildignore

@@ -2,3 +2,4 @@
 .gitignore
 .Rhistory
 ^doc$
+^Meta$

.gitignore

@@ -3,3 +3,5 @@
 *.rds
 archives/
 
+/doc/
+/Meta/

DESCRIPTION

@@ -1,11 +1,12 @@
 Package: adehabitatHR
-Version: 0.4.19
-Date: 2021-01-09
-Depends: R (>= 3.0.1), sp, methods, deldir, ade4, adehabitatMA, adehabitatLT
-Suggests: maptools, tkrplot, MASS, rgeos
+Version: 0.4.21
+Date: 2023-04-06
+Depends: R (>= 3.0.1), sp, methods, ade4, adehabitatMA, adehabitatLT
+Suggests: tkrplot, MASS, sf, deldir
 Imports: graphics, grDevices, stats
 Title: Home Range Estimation
 Author: Clement Calenge, contributions from Scott Fortmann-Roe
 Maintainer: Clement Calenge <clement.calenge@ofb.gouv.fr>
 Description: A collection of tools for the estimation of animals home range.
+VignetteBuilder: utils
 License: GPL (>= 2)

NAMESPACE

@@ -1,6 +1,6 @@
 useDynLib(adehabitatHR, .registration=TRUE)
 
-import(sp,adehabitatLT,methods,deldir,ade4,adehabitatMA)
+import(sp,adehabitatLT,methods,ade4,adehabitatMA)
 importFrom("graphics", "box", "grid", "image", "lines", "par", "title")
 importFrom("grDevices", "chull", "contourLines", "grey", "n2mfrow")
 importFrom("stats", "na.omit", "quantile", "var")

R/CharHull.r

@@ -2,8 +2,8 @@
 {
     xy <- as.data.frame(xy)
     names(xy) <- c("x","y")
-    tri <- deldir(xy[,1], xy[,2])
-    tri <- triang.list(tri)
+    tri <- deldir::deldir(xy[,1], xy[,2])
+    tri <- deldir::triang.list(tri)
     tri <- lapply(1:length(tri), function(i) {
         rbind(tri[[i]], tri[[i]][1,])
     })
@@ -77,21 +77,16 @@ CharHull <-  function(xy, unin = c("m", "km"), unout = c("ha", "m2", "km2"),
 
         resa <- .charhull(x)
 
-        pol <- as(resa, "SpatialPolygons")
-        lip <- list(pol[1])
-        for (j in 2:nrow(resa)) {
-            poo <- rbind(pol[j], lip[[j-1]])
-            pls <- slot(poo, "polygons")
-            pls1 <- lapply(pls, maptools::checkPolygonsHoles)
-            slot(poo, "polygons") <- pls1
-            lip[[j]] <- rgeos::gUnionCascaded(poo, id = rep(j,
-                                                   length(row.names(poo))))
+        resasf <- sf::st_as_sf(resa)
+        lipsf <- list(sf::st_sf(data.frame(id=1, sf::st_geometry(resasf[1,]))))
+        for (j in 2:nrow(resasf)) {
+            lipsf[[j]] <- sf::st_sf(data.frame(id=j,sf::st_union(resasf[1:j,])))
         }
-        are <- .arcpspdf(lip[[1]])
-        for (j in 2:length(lip)) {
-            are[j] <- .arcpspdf(lip[[j]])
+        lipsfg <- as(do.call(rbind, lipsf), "Spatial")
+        are <- .arcpspdf(lipsfg[1,])
+        for (j in 2:length(lipsfg)) {
+            are[j] <- .arcpspdf(lipsfg[j,])
         }
-        spP <- do.call("rbind", lip)
         if (unin == "m") {
             if (unout == "ha")
                 are <- are/10000
@@ -104,8 +99,8 @@ CharHull <-  function(xy, unin = c("m", "km"), unout = c("ha", "m2", "km2"),
             if (unout == "m2")
                 are <- are * 1e+06
         }
-        df <- data.frame(area = are, percent = resa[[2]])
-        resa <- SpatialPolygonsDataFrame(spP, df)
+        df <- data.frame(area = are, percent = resasf[[2]])
+        resa <- SpatialPolygonsDataFrame(as(lipsfg,"SpatialPolygons"), df)
         if (!is.na(pfs))
             proj4string(resa) <- CRS(pfs)
         res[[i]] <- resa

R/LoCoH.r

@@ -1,6 +1,4 @@
-
 ## k-Locoh
-
 LoCoH.k <- function(xy, k=5, unin = c("m", "km"),
                     unout = c("ha", "m2", "km2"),
                     duplicates=c("random","remove"), amount = NULL)
@@ -83,31 +81,26 @@ LoCoH.k <- function(xy, k=5, unin = c("m", "km"),
         oo <- oo[order(ar),]
         ar <- ar[order(ar)]
 
+
         ## then, "incremental" union:
-        lip <- list(SpatialPolygons(list(Polygons(list(Polygon(rbind(pol[[1]], pol[[1]][1,]))), 1))))
-        n <- k
+        lipsf <- lapply(1:length(pol), function(i)
+            sf::st_sf(data.frame(id=i,sf::st_sfc(sf::st_polygon(list(rbind(pol[[i]], pol[[i]][1,])))))))
         dej <- unlist(oo[1,])
-
+        n <- k
+        lipsf2 <- list(lipsf[[1]])
         for (i in 2:nrow(xy)) {
-            poo <- rbind(SpatialPolygons(list(Polygons(list(Polygon(rbind(pol[[i]],pol[[i]][1,]))), i))), lip[[i-1]])
-            pls <- slot(poo, "polygons")
-            pls1 <- lapply(pls, maptools::checkPolygonsHoles)
-            slot(poo, "polygons") <- pls1
-            lip[[i]] <- rgeos::gUnionCascaded(poo, id=rep(i, length(row.names(poo))))
+            ## Union des polygones progressive
+            lipsf2[[i]] <- sf::st_sf(data.frame(id=i,sf::st_union(rbind(lipsf[[i]],lipsf2[[i-1]]))))
             dej <- c(dej, unlist(oo[i,!(unlist(oo[i,])%in%dej)]))
             n[i] <- length(dej)
         }
+        lipsfg <- as(do.call(rbind, lipsf2), "Spatial")
 
         ## Compute the area
-        are <- .arcpspdf(lip[[1]])
-        for (i in 2:nrow(xy)) {
-            are[i] <- .arcpspdf(lip[[i]])
+        are <- .arcpspdf(lipsfg[1,])
+        for (j in 2:length(lipsfg)) {
+            are[j] <- .arcpspdf(lipsfg[j,])
         }
-
-        ## And the results, as a SpatialPolygonDataFrame object
-        spP <- do.call("rbind", lip)
-
-        ## The data frame:
         if (unin == "m") {
             if (unout == "ha")
                 are <- are/10000
@@ -122,7 +115,8 @@ LoCoH.k <- function(xy, k=5, unin = c("m", "km"),
         }
 
         df <- data.frame(area=are, percent=100*n/nrow(xy))
-        res <- SpatialPolygonsDataFrame(spP, df)
+        res <- SpatialPolygonsDataFrame(lipsfg, df)
+
         if (!is.na(pfs))
             proj4string(res) <- CRS(pfs)
         return(res)
@@ -261,7 +255,6 @@ MCHu.rast <- function(x, spdf, percent=100)
 
 
 ### R LoCoH
-
 LoCoH.r <- function(xy, r, unin = c("m", "km"),
                     unout = c("ha", "m2", "km2"),
                     duplicates=c("random","remove"), amount = NULL)
@@ -358,33 +351,38 @@ LoCoH.r <- function(xy, r, unin = c("m", "km"),
         if (max==0)
             stop("the distance is too small: there were only isolated points\nPlease increase the value of r")
 
+
+
         ## then, "incremental" union:
-        lip <- list(SpatialPolygons(list(Polygons(list(Polygon(rbind(pol[[1]], pol[[1]][1,]))), 1))))
+        lipsf <- lapply(1:length(pol), function(i) {
+            if (nrow(pol[[i]])>2) {
+                return(sf::st_sf(data.frame(id=i,sf::st_sfc(sf::st_polygon(list(rbind(pol[[i]], pol[[i]][1,])))))))
+            } else {
+                return(NULL)
+            }
+        })
         dej <- oo[[1]]
         n <- num[1]
+
         for (i in 2:nrow(xy)) {
-            if (nrow(pol[[i]])>2) {
-                poo <- rbind(SpatialPolygons(list(Polygons(list(Polygon(rbind(pol[[i]],pol[[i]][1,]))), i))), lip[[i-1]])
+            if (!is.null(lipsf[[i]])) {
+                poo <- rbind(lipsf[[i]], lipsf[[i-1]])
             } else {
-                poo <- lip[[i-1]]
+                poo <- lipsf[[i-1]]
             }
-            pls <- slot(poo, "polygons")
-            pls1 <- lapply(pls, maptools::checkPolygonsHoles)
-            slot(poo, "polygons") <- pls1
-
-            lip[[i]] <- rgeos::gUnionCascaded(poo, id=rep(i, length(row.names(poo))))
+            lipsf[[i]] <- sf::st_sf(data.frame(id=i,sf::st_geometry(sf::st_union(poo))))
             dej <- c(dej, oo[[i]][!(oo[[i]]%in%dej)])
             n[i] <- length(dej)
         }
 
         ## Compute the area
-        are <- .arcpspdf(lip[[1]])
+        are <- .arcpspdf(as(lipsf[[1]], "Spatial"))
         for (i in 2:nrow(xy)) {
-            are[i] <- .arcpspdf(lip[[i]])
+            are[i] <- .arcpspdf(as(lipsf[[i]], "Spatial"))
         }
 
         ## And the results, as a SpatialPolygonDataFrame object
-        spP <- do.call("rbind", lip)
+        spP <- as(do.call("rbind", lipsf),"Spatial")
 
         ## The data frame:
         n[n>max] <- max
@@ -491,7 +489,6 @@ LoCoH.r.area <- function(xy, rrange, percent=100, unin = c("m", "km"),
 
 
 ### A LoCoH
-
 LoCoH.a <- function(xy, a, unin = c("m", "km"),
                     unout = c("ha", "m2", "km2"),
                     duplicates=c("random","remove"), amount = NULL)
@@ -586,35 +583,37 @@ LoCoH.a <- function(xy, a, unin = c("m", "km"),
         num <- num[ind]
 
         ## then, "incremental" union:
-        lip <- list(SpatialPolygons(list(Polygons(list(Polygon(rbind(pol[[1]], pol[[1]][1,]))), 1))))
+        lipsf <- lapply(1:length(pol), function(i) {
+            if (nrow(pol[[i]])>2) {
+                return(sf::st_sf(data.frame(id=i,sf::st_sfc(sf::st_polygon(list(rbind(pol[[i]], pol[[i]][1,])))))))
+            } else {
+                return(NULL)
+            }
+        })
         dej <- oo[[1]]
         n <- num[1]
+
         for (i in 2:nrow(xy)) {
-            if (nrow(pol[[i]])>3) {
-                poo <- rbind(SpatialPolygons(list(Polygons(list(Polygon(rbind(pol[[i]],pol[[i]][1,]))), i))), lip[[i-1]])
+            if (!is.null(lipsf[[i]])) {
+                poo <- rbind(lipsf[[i]], lipsf[[i-1]])
             } else {
-                poo <- lip[[i-1]]
+                poo <- lipsf[[i-1]]
             }
-            pls <- slot(poo, "polygons")
-            pls1 <- lapply(pls, maptools::checkPolygonsHoles)
-            slot(poo, "polygons") <- pls1
-
-            lip[[i]] <- rgeos::gUnionCascaded(poo, id=rep(i, length(row.names(poo))))
+            lipsf[[i]] <- sf::st_sf(data.frame(id=i,sf::st_geometry(sf::st_union(poo))))
             dej <- c(dej, oo[[i]][!(oo[[i]]%in%dej)])
             n[i] <- length(dej)
         }
 
         ## Compute the area
-        are <- .arcpspdf(lip[[1]])
+        are <- .arcpspdf(as(lipsf[[1]], "Spatial"))
         for (i in 2:nrow(xy)) {
-            are[i] <- .arcpspdf(lip[[i]])
+            are[i] <- .arcpspdf(as(lipsf[[i]], "Spatial"))
         }
 
         ## And the results, as a SpatialPolygonDataFrame object
-        spP <- do.call("rbind", lip)
+        spP <- as(do.call("rbind", lipsf),"Spatial")
 
         ## The data frame:
-        df <- data.frame(area=are, percent=100*n/nrow(xy))
         if (unin == "m") {
             if (unout == "ha")
                 are <- are/10000
@@ -629,6 +628,7 @@ LoCoH.a <- function(xy, a, unin = c("m", "km"),
         }
 
 
+        df <- data.frame(area=are, percent=100*n/nrow(xy))
         res <- SpatialPolygonsDataFrame(spP, df)
         if (!is.na(pfs))
             proj4string(res) <- CRS(pfs)

man/LoCoH.Rd

@@ -98,7 +98,7 @@ LoCoH.a.area(xy, arange, percent=100, unin = c("m", "km"),
 }
 \note{
 
-  These functions rely on the packages rgeos, gpclib, and maptools.
+  These functions rely on the package sf.
 
   The LoCoH family of methods for locating Utilization Distributions
   consists of three algorithms: Fixed k LoCoH, Fixed r LoCoH, and

man/kernelUD.Rd

@@ -207,7 +207,7 @@ estUDm2spixdf(x)
 
   Silverman, B.W. (1986)
   \emph{Density estimation for statistics and data
-    analysis}. London: Chapman \& Hall.
+    analysis}. London: Chapman and Hall.
 
   Worton, B.J. (1989) Kernel methods for estimating the utilization
   distribution in home-range studies. \emph{Ecology}, \bold{70}, 164--168.

src/tests.c

@@ -893,7 +893,7 @@ void prodmatAAtB (double **a, double **b)
 void prodmatAtBrandomC (double **a, double **b, double **c, int*permut)
 /*--------------------------------------------------
  * Produit matriciel AtB
- * les lignes de B sont permutÚes par la permutation permut
+ * les lignes de B sont permutes par la permutation permut
  --------------------------------------------------*/
 {
     int j, k, i, i0, lig, col, col2;
@@ -1353,7 +1353,7 @@ void CVmise(int *nloc, double *xlo, double *ylo,
     int i, nlo, nh, ndist;
     double *xl, *yl, h, *dists;
 
-    /* Allocation de mémoire */
+    /* Allocation de memoire */
     nlo = *nloc;
     nh = *nhteste;
 
@@ -2691,7 +2691,7 @@ SEXP filtreLmin(SEXP df, double Lmin, SEXP PA2, SEXP fll)
     int nrow, i, k, nfl, fl;
     double sx, sy, ggg;
 
-    /* récup des vecteurs */
+    /* recup des vecteurs */
     PROTECT(x = coerceVector(VECTOR_ELT(df,0), REALSXP));
     PROTECT(y = coerceVector(VECTOR_ELT(df,1), REALSXP));
     PROTECT(date = coerceVector(VECTOR_ELT(df,2), REALSXP));
@@ -2716,13 +2716,13 @@ SEXP filtreLmin(SEXP df, double Lmin, SEXP PA2, SEXP fll)
 	}
     }
 
-    /* allocation mémoire */
+    /* allocation memoire */
     PROTECT(xn = allocVector(REALSXP, k));
     PROTECT(yn = allocVector(REALSXP, k));
     PROTECT(daten = allocVector(REALSXP, k));
     PROTECT(PAn = allocVector(REALSXP, k));
 
-    /* préparation de la boucle */
+    /* preparation de la boucle */
     REAL(xn)[0] = REAL(x)[0];
     REAL(yn)[0] = REAL(y)[0];
     REAL(daten)[0] = REAL(date)[0];
@@ -2739,8 +2739,8 @@ SEXP filtreLmin(SEXP df, double Lmin, SEXP PA2, SEXP fll)
 
     for (i=1; i< nrow; i++) {
 
-	/* si la distance entre localisations successives est inférieure à Lmin, on 
-	   se contente d'en calculer la moyenne (que l'on stockera à la première date)
+	/* si la distance entre localisations successives est inferieure a Lmin, on 
+	   se contente d'en calculer la moyenne (que l'on stockera a la premiere date)
 	 */
 	ggg = 1;
 	if (length(PA) > 1) {
@@ -2753,8 +2753,8 @@ SEXP filtreLmin(SEXP df, double Lmin, SEXP PA2, SEXP fll)
 	    sy = sy + REAL(y)[i];
 	} else {
 
-	    /* Si la nouvelle localisation est la première éloignée après un clusters de Lmin,
-	       On commence par stocker la précédente (moyenne des positions)
+	    /* Si la nouvelle localisation est la premiere eloignee apres un clusters de Lmin,
+	       On commence par stocker la precedente (moyenne des positions)
 	     */
 	    if (nfl > 1) {
 		REAL(xn)[k] = sx/((double) nfl);
@@ -2774,13 +2774,13 @@ SEXP filtreLmin(SEXP df, double Lmin, SEXP PA2, SEXP fll)
 		REAL(PAn)[k] = 1;
 	    }
 
-	    /* et au cas où, on garde cette loc en mémoire */
+	    /* et au cas ou, on garde cette loc en memoire */
 	    sx = REAL(x)[i];
 	    sy = REAL(y)[i];
 	}
     }
 
-    /* On renvoie alors les résultats dans un data.frame */
+    /* On renvoie alors les resultats dans un data.frame */
     PROTECT(df2 = allocVector(VECSXP, 4));
     SET_VECTOR_ELT(df2, 0, xn);
     SET_VECTOR_ELT(df2, 1, yn);
@@ -2962,7 +2962,7 @@ SEXP fillsegments(SEXP df2, SEXP Tmaxr, SEXP taur, SEXP hminr, SEXP D, SEXP Lmin
 }
 
 
-/* On calcule maintenant, sur la base d'une grille passée, l'estimation kernel */
+/* On calcule maintenant, sur la base d'une grille passee, l'estimation kernel */
 SEXP mkde(SEXP xyh, SEXP grid)
 {