version 0.0-7

DESCRIPTION

@@ -1,20 +1,21 @@
 Package: adespatial
 Title: Multivariate Multiscale Spatial Analysis
-Version: 0.0-6
-Date: 2016-11-27
+Version: 0.0-7
+Date: 2016-12-13
 Author: Stéphane Dray, Guillaume Blanchet, Daniel Borcard, Guillaume Guenard,
-    Thibaut Jombart, Guillaume Larocque, Pierre Legendre, Helene H Wagner
+    Thibaut Jombart, Guillaume Larocque, Pierre Legendre, Naima Madi, Helene H Wagner
 Description: Tools for the multiscale spatial analysis of multivariate data.
     Several methods are based on the use of a spatial weighting matrix and its
     eigenvector decomposition (Moran's Eigenvectors Maps, MEM).
 Maintainer: Stéphane Dray <stephane.dray@univ-lyon1.fr>
-Imports: ade4 (>= 1.7-1), adegraphics, sp, spdep, lattice, methods,
+Imports: ade4 (>= 1.7-5), adegraphics, sp, spdep, lattice, methods,
         grDevices, graphics, MASS, stats, utils, shiny
-Suggests: vegan
+Suggests: vegan, knitr, maptools
 License: GPL (>= 2)
 Encoding: UTF-8
 NeedsCompilation: yes
 RoxygenNote: 5.0.1
-Packaged: 2016-11-27 08:45:50 UTC; stephane
+VignetteBuilder: knitr
+Packaged: 2016-12-13 16:18:13 UTC; stephane
 Repository: CRAN
-Date/Publication: 2016-11-27 15:04:07
+Date/Publication: 2016-12-13 19:53:41

MD5

@@ -1,4 +1,4 @@
-9aac453bc0436517b120e83e025c441a *DESCRIPTION
+5ec32fcd0480004cf9bd8d5ba01de8c9 *DESCRIPTION
 139d5f6238d79b754999d4d5daf73089 *NAMESPACE
 94bdf730f42fc22f123f53e7c7601ef4 *R/Cperiodogram.R
 7ceb69c66ad3c931dadf526f24b76566 *R/LCBD.comp.R
@@ -9,31 +9,35 @@ bb83be16b3d30642dedf2c544fd3846b *R/chooseCN.R
 573df8b26a46555a18cbb1a6a6f50280 *R/create.dbMEM.model.R
 bc5b17d3b2ba5255779702731ea08132 *R/createlistw.R
 7876330ca5cdd7b7bb05d3f33fdb4edd *R/dbmem.R
-e3d5c6dedbde76c85257af6e5a058556 *R/dist.ldc.R
+34a0a76c9bc3cf4bd376353772be77b2 *R/dist.ldc.R
 d5e1b54f06fa6795f85d480fb0fe4c38 *R/forward.sel.R
 ff78faeb593d3169f48ea65612f16279 *R/forward.sel.par.R
 d496c92a0d76f88dcb73241305272abb *R/give.thresh.R
 7a7b322db4984c59a376ca9551559109 *R/globloc.rtests.R
 e058a42ffb262ce6b737fe4fd81b4dd5 *R/mfpa.R
 1648ae668a4072cbc6d4331a42ef9385 *R/moran.bounds.R
-347435f1d32d59724f6a5add09a30b0d *R/moran.randtest.R
-2f837157693c3ccd188735cdb05c8354 *R/moranNP.randtest.R
+0d47d7748bed877b4bfd5611622720b0 *R/moran.randtest.R
+9a65c8d1f414cd09fa65b533e00dfb34 *R/moranNP.randtest.R
 3334f298652625c59a9a969a736d2afb *R/mspa.R
 c998600ee8edd2beee3935d6b6f8bbe8 *R/msr.R
 37a8f3d128e9249b52479740be77a338 *R/mst.nb.R
-0750dbba5f91db336d4c1fe685b7eca1 *R/multispati.R
+1b8a7e41f9ec19d87383bda0425f0844 *R/multispati.R
 1a67a93009ca79e034315a1dc47fd85b *R/ortho.AIC.R
 3b64b529dc5471321eb746de9e22b76a *R/orthobasis.poly.R
 0db736777b4cd029d1305dc77a816f49 *R/plot.orthobasisSp.R
-6b02ce4fadf61ec0bdb01bc2d025909d *R/scalogram.R
+803fefb69b8f3a50f9b7aa0e57099c32 *R/scalogram.R
 fdb18fbf6532d0f5eeba2686ec943997 *R/scores.listw.R
 3490c2cdf5b29c78d3b95b35dffbb9c1 *R/stimodels.R
 2b91784792ca3864509329821ec86d99 *R/test.W.R
 26dd55b1d39d83f4344ef244a485cbef *R/trichoptera.R
 6ef116667e2b6e52ee58567610e99c63 *R/variogmultiv.R
+70f41a192c7b61bfa0d6f01c64da07f3 *build/vignette.rds
 a143c7c17f33a154ac60a28e7e94b5b8 *data/trichoptera.rda
 2622e811a5bcdc418bd4db98b84d2667 *inst/createlistw/app.R
 c0f6fa59550c390fe762150b34b65653 *inst/createlistw/www/style.css
+e0520f20735334c85dc38f2b64586705 *inst/doc/tutorial.R
+8ef6f665bf8c88d7e4a5320072e5781b *inst/doc/tutorial.Rmd
+385ef4ac3a7c03e9170de2d719276d06 *inst/doc/tutorial.html
 6d4bd51bf9f332422146d9651b2c049b *man/Cperiodogram.Rd
 f6d7f83d67eda8a5e837592fb81bc0ec *man/LCBD.comp.Rd
 d196d38cf2fd2ec5098c6d84531466f2 *man/WRperiodogram.Rd
@@ -43,24 +47,24 @@ d196d38cf2fd2ec5098c6d84531466f2 *man/WRperiodogram.Rd
 a9e7142735998d3ccb7f387c999368d7 *man/create.dbMEM.model.Rd
 2aa7d8df9eef6e9ea67497ffac46ee3e *man/createlistw.Rd
 9c7bdaf69e42cdf1336ff0b2adb2442a *man/dbmem.Rd
-a877e4bc64d83d5da92fa25bd428b3a8 *man/dist.ldc.Rd
+852d9eee0c77b58cc070b78eff75e4ad *man/dist.ldc.Rd
 ae80caadf686ce98b121762b62859fc9 *man/forward.sel.Rd
 61f106b99f673bd280bbdb3a87ca9a89 *man/forward.sel.par.Rd
 0fa9c8f23512a7f1c842e4af41ab78c9 *man/give.thresh.Rd
 ead5de22b85181cc6fbe60e5741b9f55 *man/global.rtest.Rd
 0302930bad64bf2b4bc9f51a985387a0 *man/mem.Rd
 d1b80f37aef3e356fb4f4cfe86d4ba12 *man/mfpa.Rd
 71bb13d47ae2233db0912a5da3b371e0 *man/moran.bounds.Rd
-89d82e990889e87aaf15cd72f8f0e37b *man/moran.randtest.Rd
-5ec7b05ddefd25e21bd6b6533f7e2da1 *man/moranNP.randtest.Rd
+c8ce80d2307c90d9ab26871f672bf634 *man/moran.randtest.Rd
+b49398bb164ac295d2963b5d975e133f *man/moranNP.randtest.Rd
 c9778d8f952cff482a78704d699c1335 *man/mspa.Rd
 5f3c88716cc989a0cca0913c29b5f669 *man/msr.Rd
 f24c163cc329630ba6b2ca67dbfae1aa *man/mst.nb.Rd
-0eeb22d5bb35401293850249b304fe6d *man/multispati.Rd
+7953a3ac83fb67bf72f2e23496c6a22d *man/multispati.Rd
 8f1f625cb70def5f6ad3c992d9143dc4 *man/ortho.AIC.Rd
 cba8fa14a000c1f354394168b3d570d2 *man/orthobasis.poly.Rd
 02f217e761c8bfdb93fa3d081b250a20 *man/plot.orthobasisSp.Rd
-f29d79169eace08578f9b139c5c54fbf *man/scalogram.Rd
+53038c9601bfb83f087a03f51bb49789 *man/scalogram.Rd
 d0a34d89074e0f3aa036402019486d30 *man/stimodels.Rd
 c585f9528688d42c6bef7849363f9bbb *man/test.W.Rd
 0fed071f3d1ae7138155c304cedcf1ad *man/trichoptera.Rd
@@ -72,3 +76,7 @@ e2dfc261ddf744e7e8cf683319c642a3 *src/betadiv.c
 1b414d81aa1ee8b8f203ebe0ba69185a *src/forward.c
 74855ba1084ab879d1dc561afa798eda *src/forward.h
 84e8f733b01f8d0aa484f7cc5e1951ac *src/testsmoran.c
+3783c47c5fa01a30ba5f6084f7cb2aa6 *vignettes/adespatial.bib
+5f8e3c8ef7e0e9bc860dbbb9526ceddb *vignettes/adespatial.png
+fa718395bbdb0ee64c9fa87be3ff429e *vignettes/createlistw.png
+8ef6f665bf8c88d7e4a5320072e5781b *vignettes/tutorial.Rmd

R/dist.ldc.R

@@ -2,7 +2,7 @@
 #' 
 #' Compute dissimilarity indices for ecological data matrices. The dissimilarity
 #' indices computed by this function are those described in Legendre & De 
-#' Cáceres (2013). In the name of the function, 'dlc' stands for the author's 
+#' Cáceres (2013). In the name of the function, 'ldc' stands for the author's 
 #' names. Twelve of these 21 indices are not readily available in other R 
 #' package functions; four of them can, however, be computed in two computation 
 #' steps in \code{vegan}.
@@ -100,7 +100,7 @@
 #'   dissimilarities have a maximum value of either 1 or sqrt(2). Three
 #'   dissimilarities (Euclidean, Manhattan, Modified mean character difference)
 #'   do not have an upper bound and are thus inappropriate for beta diversity
-#'   studies. The chi-square distance has an upper bound of sqrt(2*(sum(Y))). 
+#'   studies. The chi-square distance has an upper bound of sqrt(2*(sum(Y))).\cr 
 #'   The Euclidean, Hellinger, chord, chi-square and species profiles
 #'   dissimilarities have the property of being Euclidean, meaning that they
 #'   never produce negative eigenvalues in principal coordinate analysis. The
@@ -124,7 +124,7 @@
 #'   transforms the abundance data to binary form, and using one of the
 #'   quantitative indices of the present function. Table 1 of Legendre & De
 #'   Cáceres (2013) shows the incidence-based (presence-absence-based) indices
-#'   computed by the various indices using binary data. The Euclidean distance
+#'   computed by the various indices using binary data. \cr The Euclidean distance
 #'   computed on untransformed presence-absence or abundance data produces
 #'   non-informative and incorrect ordinations, as shown in Legendre & Legendre
 #'   (2012, p. 300) and in Legendre & De Cáceres (2013). However, the Euclidean

R/moran.randtest.R

@@ -22,7 +22,7 @@
 #' 
 #' if(require("ade4", quietly = TRUE)  & require("spdep", quiet = TRUE)){
 #' data(mafragh)
-#' tests <- moran.randtest(mafragh$mil, nb2listw(mafragh$nb))
+#' tests <- moran.randtest(mafragh$env, nb2listw(mafragh$nb))
 #' tests
 #' plot(tests)
 #' 

R/moranNP.randtest.R

@@ -31,10 +31,10 @@
 #' @examples
 #' if(require("ade4", quietly = TRUE)  & require("spdep", quiet = TRUE)){
 #' data(mafragh)
-#' tests <- moranNP.randtest(mafragh$mil[,1], nb2listw(mafragh$nb),
+#' tests <- moranNP.randtest(mafragh$env[,1], nb2listw(mafragh$nb),
 #'  alter = "two-sided", p.adjust.method = "holm")
 #' tests
-#' moran.randtest(mafragh$mil[,1], nb2listw(mafragh$nb))$obs
+#' moran.randtest(mafragh$env[,1], nb2listw(mafragh$nb))$obs
 #' sum(tests$obs)
 #' }
 #' 

R/multispati.R

@@ -88,9 +88,9 @@
 #'     data(mafragh)
 #'     maf.xy <- mafragh$xy
 #'     maf.flo <- mafragh$flo
-#'     maf.listw <- nb2listw(neig2nb(mafragh$neig))
+#'     maf.listw <- nb2listw(mafragh$nb)
 #'     if(adegraphicsLoaded()) {
-#'       g1 <- s.label(maf.xy, nb = neig2nb(mafragh$neig), plab.cex = 0.75)
+#'       g1 <- s.label(maf.xy, nb = mafragh$nb, plab.cex = 0.75)
 #'     } else {
 #'       s.label(maf.xy, neig = mafragh$neig, clab = 0.75)
 #'     }
@@ -176,7 +176,7 @@
 #'       par(mfrow = c(1, 1))
 #'     }
 #' 
-#'     maf.pca <- dudi.pca(mafragh$mil, scannf = FALSE)
+#'     maf.pca <- dudi.pca(mafragh$env, scannf = FALSE)
 #'     multispati.randtest(maf.pca, maf.listw)
 #'     maf.pca.ms <- multispati(maf.pca, maf.listw, scannf=FALSE)
 #'     plot(maf.pca.ms)

R/scalogram.R

@@ -48,7 +48,7 @@
 #' me <- mem(nb2listw(mafragh$nb))
 #' 
 #' if(require("adegraphics", quietly = TRUE)){
-#' sc1 <- scalogram(mafragh$mil$Conduc, me, nblocks = 10)
+#' sc1 <- scalogram(mafragh$env$Conduc, me, nblocks = 10)
 #' plot(sc1) 
 #' }
 #' }