Spell check corrections, with en-GB language

8b511f92 · philippe.verley_ird.fr · 73b25efa · 8b511f92 · 8b511f92 · 8b511f92
Commit 8b511f92 authored 4 years ago by philippe.verley_ird.fr
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -17,3 +17,4 @@ License: GPL-2
 NeedsCompilation: yes
 Repository: CRAN
 RoxygenNote: 7.1.1
+Language: en-GB
--- a/man/Allogny.Rd
+++ b/man/Allogny.Rd
@@ -4,7 +4,7 @@
 \docType{data}
 \title{Spatial pattern of oaks suffering from frost shake in Allogny, France.}
 \description{
-Spatial pattern of sound and splited oaks (\emph{Quercus petraea}) suffering from frost shake in a 2.35-ha plot in Allogny, France.
+Spatial pattern of sound and split oaks (\emph{Quercus petraea}) suffering from frost shake in a 2.35-ha plot in Allogny, France.
 }
 \usage{data(Allogny)}
 \format{
@@ -14,10 +14,10 @@ A list with 4 components:\cr
 \code{$status } is a factor with 2 levels \eqn{("splited","sound")}.\cr
 }
 \source{
-  Grandjean, G., Jabiol, B., Bruchiamacchie, M. and Roustan, F. 1990. \emph{Recherche de corrlations entre les paramtres daphiques, et plus spcialement texture, hydromorphie et drainage interne, et la rponse individuelle des chenes sessiles et pdonculs  la glivure.} Rapport de recherche ENITEF, Nogent sur Vernisson, France.
+  Grandjean, G., Jabiol, B., Bruchiamacchie, M. and Roustan, F. 1990. \emph{Recherche de correlations entre les parametres edaphiques, et plus specialement texture, hydromorphie et drainage interne, et la reponse individuelle des chenes sessiles et pedoncules ? la gelivure.} Rapport de recherche ENITEF, Nogent sur Vernisson, France.
 }
 \references{
-Goreaud, F. & Plissier, R. 2003. Avoiding misinterpretation of biotic interactions with the intertype \emph{K12}-function: population independence vs. random labelling hypotheses. \emph{Journal of Vegetation Science}, 14: 681-692.
+Goreaud, F. & Pelissier, R. 2003. Avoiding misinterpretation of biotic interactions with the intertype \emph{K12}-function: population independence vs. random labelling hypotheses. \emph{Journal of Vegetation Science}, 14: 681-692.
 }
 \examples{
 data(Allogny)

--- a/man/BPoirier.Rd
+++ b/man/BPoirier.Rd
@@ -10,21 +10,21 @@ Spatial pattern of 162 beeches, 72 oaks and 3 hornbeams in a 1-ha 140 yr-old tem
 \format{
 A list with 8 components:\cr
 \code{$rect    } is a vector of coordinates \eqn{(xmin,ymin,xmax,ymax)} of the origin and the opposite corner of a 110 by 90 m rectangular plot.\cr
-\code{$tri1    } is a list of vertice coordinates \eqn{(ax,ay,bx,by,cx,cy)} of contiguous triangles covering the denser part of the plot.\cr
-\code{$tri2    } is a list of vertice coordinates \eqn{(ax,ay,bx,by,cx,cy)} of contiguous triangles covering the sparser part of the plot.\cr
-\code{$poly1   } is a list of vertice coordinates \eqn{(x,y)} of the polygon enclosing \code{BPoirier$tri1}.\cr
-\code{$poly2   } is a list of two polygons vertice coordinates \eqn{(x,y)} enclosing \code{BPoirier$tri2}.\cr
+\code{$tri1    } is a list of vertex coordinates \eqn{(ax,ay,bx,by,cx,cy)} of contiguous triangles covering the denser part of the plot.\cr
+\code{$tri2    } is a list of vertex coordinates \eqn{(ax,ay,bx,by,cx,cy)} of contiguous triangles covering the sparser part of the plot.\cr
+\code{$poly1   } is a list of vertex coordinates \eqn{(x,y)} of the polygon enclosing \code{BPoirier$tri1}.\cr
+\code{$poly2   } is a list of two polygons vertex coordinates \eqn{(x,y)} enclosing \code{BPoirier$tri2}.\cr
 \code{$trees   } is a list of tree coordinates \eqn{(x,y)}.\cr
 \code{$species } is a factor with 3 levels \eqn{("beech","oak","hornbeam")} corresponding to species names of the trees.\cr
 \code{$dbh     } is a vector of tree size (diameter at breast height in cm). 
 }
 \source{
-Pardé, J. 1981. De 1882 à 1976/80 : les places d'expèrience de sylviculture du hetre en foret domainiale de Haye. \emph{Revue Forestière Française}, 33: 41-64.
+Parde, J. 1981. De 1882 a 1976/80 : les places d'experience de sylviculture du hetre en foret domaniale de Haye. \emph{Revue Forestiere Francaise}, 33: 41-64.
 }

 \references{
-Goreaud, F. 2000. \emph{Apports de l'analyse de la structure spatiale en foret tempérée à l'étude et la modélisation des peuplements complexes}. Thèse de doctorat, ENGREF, Nancy, France.\cr\cr
-Pélissier, R. & Goreaud, F. 2001. A practical approach to the study of spatial structure in simple cases of heterogeneous vegetation. \emph{Journal of Vegetation Science}, 12: 99-108.
+Goreaud, F. 2000. \emph{Apports de l'analyse de la structure spatiale en foret temperee a l'etude et la modelisation des peuplements complexes}. These de doctorat, ENGREF, Nancy, France.\cr\cr
+Pelissier, R. & Goreaud, F. 2001. A practical approach to the study of spatial structure in simple cases of heterogeneous vegetation. \emph{Journal of Vegetation Science}, 12: 99-108.
 }
 \examples{
 data(BPoirier)

--- a/man/Couepia.Rd
+++ b/man/Couepia.Rd
@@ -10,15 +10,15 @@ Spatial pattern of 34 mature individuals and 173 young individuals of the tree s
 \format{
 A list with 4 components:\cr
 \code{$rect  } is a vector of coordinates \eqn{(xmin,ymin,xmax,ymax)} of the origin and the opposite corner of a 500 by 500 m rectangular plot.\cr
-\code{$tri   } is a list of vertice coordinates \eqn{(ax,ay,bx,by,cx,cy)} of contiguous triangles covering swampy parts of the plot.\cr
+\code{$tri   } is a list of vertex coordinates \eqn{(ax,ay,bx,by,cx,cy)} of contiguous triangles covering swampy parts of the plot.\cr
 \code{$trees } is a list of tree coordinates \eqn{(x,y)}.\cr
 \code{$stage } is a factor with 2 levels \eqn{("mature","young")}.\cr
 }
 \source{
-  Collinet, F. 1997. \emph{Essai de regroupement des principales espèces structurantes d'une foret dense humide d'après l'analyse de leur répartition spatiale (foret de Paracou - Guyane).} Thèse de doctorat, Université Claude Bernard, Lyon, France.
+  Collinet, F. 1997. \emph{Essai de regroupement des principales especes structurantes d'une foret dense humide d'apres l'analyse de leur repartition spatiale (foret de Paracou - Guyane).} These de doctorat, Universite Claude Bernard, Lyon, France.
 }
 \references{
-Goreaud, F. & Pélissier, R. 2003. Avoiding misinterpretation of biotic interactions with the intertype \emph{K12}-function: population independence vs. random labelling hypotheses. \emph{Journal of Vegetation Science}, 14: 681-692.
+Goreaud, F. & P?Pelissier, R. 2003. Avoiding misinterpretation of biotic interactions with the intertype \emph{K12}-function: population independence vs. random labelling hypotheses. \emph{Journal of Vegetation Science}, 14: 681-692.
 }
 \examples{
 data(Couepia)

--- a/man/Paracou15.Rd
+++ b/man/Paracou15.Rd
@@ -4,7 +4,7 @@
 \docType{data}
 \title{Tree spatial pattern in control plot 15, Paracou experimental station, French Guiana}
 \description{
-Spatial pattern of 4128 trees of 332 diffrent species in a 250 m X 250 m control plot in Paracou experimental station, French Guiana. 
+Spatial pattern of 4128 trees of 332 different species in a 250 m X 250 m control plot in Paracou experimental station, French Guiana. 
 }
 \usage{data(Paracou15)}
 \format{
@@ -15,11 +15,11 @@ A list with 5 components:\cr
 \code{$spdist } is an object of class \code{"dist"} giving between-species distances based on functional traits (see Paine et al. 2011).\cr
 }
 \source{
-Gourlet-Fleury, S., Ferry, B., Molino, J.-F., Petronelli, P. & Schmitt, L. 2004. \emph{Exeprimental plots: key features.} Pp. 3-60 In Gourlet-Fleury, S., Guehl, J.-M. & Laroussinie, O. (Eds.), Ecology and Managament of a Neotropical rainforest - Lessons drawn from Paracou, a long-term experimental research site in French Guiana. Elsevier SAS, France.
+Gourlet-Fleury, S., Ferry, B., Molino, J.-F., Petronelli, P. & Schmitt, L. 2004. \emph{Experimental plots: key features.} Pp. 3-60 In Gourlet-Fleury, S., Guehl, J.-M. & Laroussinie, O. (Eds.), Ecology and Management of a Neotropical rainforest - Lessons drawn from Paracou, a long-term experimental research site in French Guiana. Elsevier SAS, France.
 }

 \references{
-Paine, C. E. T., Baraloto, C., Chave, J. & Hérault, B. 2011. Functional traits of individual trees reveal ecological constraints on community assembly in tropical rain forests. \emph{Oikos}, 120: 720-727.\cr\cr
+Paine, C. E. T., Baraloto, C., Chave, J. & Herault, B. 2011. Functional traits of individual trees reveal ecological constraints on community assembly in tropical rain forests. \emph{Oikos}, 120: 720-727.\cr\cr
 }
 \examples{
 data(Paracou15)

--- a/man/dval.Rd
+++ b/man/dval.Rd
@@ -22,20 +22,20 @@ dval(p, upto, by, nx, ny)
 \details{
 The local density is estimated for a regular sequence of sample circles radii given by \code{seq(by,upto,by)} (see \code{\link{seq}}).
 The sample circles are centred at the nodes of a regular grid with size \eqn{nx} by \eqn{ny}. Ripley's edge effect correction is applied when 
- the sample circles overlap boundary of the sampling window (see Ripley (1977) or Goreaud & Pélissier (1999) for an extension to circular and complex 
+ the sample circles overlap boundary of the sampling window (see Ripley (1977) or Goreaud & P?Pelissier (1999) for an extension to circular and complex 
 sampling windows). Due to edge effect correction, \code{upto}, the maximum radius of the sample circles, is half the longer side for a rectangle sampling
 window (i.e. \eqn{0.5*max((xmax-xmin),(ymax-ymin))}) and the radius \eqn{r0} for a circular sampling window (see \code{\link{swin}}).
 }
 \value{
 A list of class \code{c("vads","dval")} with essentially the following components:
 \item{r }{a vector of regularly spaced out distances (\code{seq(by,upto,by)}).}
- \item{xy }{a data frame of \eqn{(nx*ny)} observations giving \eqn{(x,y)} coordinates of the centers of the sample circles (the grid nodes).}
+ \item{xy }{a data frame of \eqn{(nx*ny)} observations giving \eqn{(x,y)} coordinates of the centres of the sample circles (the grid nodes).}
 \item{cval }{a matrix of size \eqn{(nx*ny,length(r))} giving the estimated number of points of the pattern per sample circle with radius \eqn{r}.}
 \item{dval }{a matrix of size \eqn{(nx*ny,length(r))} giving the estimated number of points of the pattern per unit area per sample circle with radius \eqn{r}.}
 }
 \references{
-  Goreaud, F. and Pélissier, R. 1999. On explicit formula of edge effect correction for Ripley's \emph{K}-function. \emph{Journal of Vegetation Science}, 10:433-438.\cr\cr
-  Pélissier, R. and Goreaud, F. 2001. A practical approach to the study of spatial structure in simple cases of heterogeneous vegetation. \emph{Journal of Vegetation Science}, 12:99-108.\cr\cr
+  Goreaud, F. and P?Pelissier, R. 1999. On explicit formula of edge effect correction for Ripley's \emph{K}-function. \emph{Journal of Vegetation Science}, 10:433-438.\cr\cr
+  P?Pelissier, R. and Goreaud, F. 2001. A practical approach to the study of spatial structure in simple cases of heterogeneous vegetation. \emph{Journal of Vegetation Science}, 12:99-108.\cr\cr
  Ripley, B.D. 1977. Modelling spatial patterns. \emph{Journal of the Royal Statistical Society B}, 39:172-212.
 }
 \author{\email{Raphael.Pelissier@ird.fr}}

--- a/man/inside.swin.Rd
+++ b/man/inside.swin.Rd
 \encoding{latin1}
 \name{inside.swin}
 \alias{inside.swin}
-\title{Test wether points are inside a sampling window}
+\title{Test whether points are inside a sampling window}
 \description{
 Function \code{inside.swin} tests whether points lie inside or outside a given sampling window.
 }

--- a/man/k12fun.Rd
+++ b/man/k12fun.Rd
 \encoding{latin1}
 \name{k12fun}
 \alias{k12fun}
-\title{Multiscale second-order neigbourhood analysis of a bivariate spatial point pattern}
+\title{Multiscale second-order neighbourhood analysis of a bivariate spatial point pattern}
 \description{
-  Computes estimates of the intertype \emph{K12}-function and associated neigbourhood functions from a bivariate spatial point pattern 
+  Computes estimates of the intertype \emph{K12}-function and associated neighbourhood functions from a bivariate spatial point pattern 
  in a simple (rectangular or circular) or complex sampling window. Computes optionally local confidence limits of the functions
  under the null hypotheses of population independence or random labelling (see Details).
 }
@@ -28,7 +28,7 @@ k12fun(p, upto, by, nsim=0, H0=c("pitor","pimim","rl"), prec=0.01, nsimax=3000,
   of points of type 1 and 2, respectively.}
 }
 \details{
-  Function \code{k12fun} computes the intertype \eqn{K12(r)} function of second-order neigbourhood analysis and the associated functions \eqn{g12(r)},
+  Function \code{k12fun} computes the intertype \eqn{K12(r)} function of second-order neighbourhood analysis and the associated functions \eqn{g12(r)},
   \eqn{n12(r)} and \eqn{L12(r)}.\cr\cr
  For a homogeneous isotropic bivariate point process of intensities \eqn{\lambda1} and \eqn{\lambda2},
  the second-order property could be characterized by a function \eqn{K12(r)} (Lotwick & Silverman 1982), so that the expected
@@ -43,7 +43,7 @@ k12fun(p, upto, by, nsim=0, H0=c("pitor","pimim","rl"), prec=0.01, nsimax=3000,
  \eqn{O12(r) = \lambda2*g12(r)} (Wiegand & Moloney 2004).\cr\cr
  
  The program introduces an edge effect correction term according to the method proposed by Ripley (1977)
-  and extended to circular and complex sampling windows by Goreaud & Pélissier (1999).\cr\cr
+  and extended to circular and complex sampling windows by Goreaud & Pelissier (1999).\cr\cr
 
  Theoretical values under the null hypothesis of either population independence or random labelling as well as
  local Monte Carlo confidence limits and p-values of departure from the null hypothesis (Besag & Diggle 1977) are estimated at each distance \eqn{r}.\cr
@@ -54,7 +54,7 @@ k12fun(p, upto, by, nsim=0, H0=c("pitor","pimim","rl"), prec=0.01, nsimax=3000,
  to mimic the pattern of type 1 points (see \code{\link{mimetic}}.\cr
  The random labelling hypothesis \code{"rl"} assumes that the probability to bear a given mark is the same for all points of the pattern and
   doesn't depends on neighbours. It is therefore tested conditionally to the whole spatial pattern, by randomizing the marks over the points'
-  locations kept unchanged (see Goreaud & Pélissier 2003 for further details). 
+  locations kept unchanged (see Goreaud & Pelissier 2003 for further details). 
 }
 \value{
  A list of class \code{"fads"} with essentially the following components:
@@ -72,8 +72,8 @@ k12fun(p, upto, by, nsim=0, H0=c("pitor","pimim","rl"), prec=0.01, nsimax=3000,
 }
 \references{
 Besag J.E. & Diggle P.J. 1977. Simple Monte Carlo tests spatial patterns. \emph{Applied Statistics}, 26:327-333.\cr\cr
- Goreaud F. & Pélissier R. 1999. On explicit formulas of edge effect correction for Ripley's K-function. \emph{Journal of Vegetation Science}, 10:433-438.\cr\cr
- Goreaud, F. & Pélissier, R. 2003. Avoiding misinterpretation of biotic interactions with the intertype \emph{K12}-function: population independence vs. random labelling hypotheses. \emph{Journal of Vegetation Science}, 14: 681-692.\cr\cr
+ Goreaud F. & Pelissier R. 1999. On explicit formulas of edge effect correction for Ripley's K-function. \emph{Journal of Vegetation Science}, 10:433-438.\cr\cr
+ Goreaud, F. & Pelissier, R. 2003. Avoiding misinterpretation of biotic interactions with the intertype \emph{K12}-function: population independence vs. random labelling hypotheses. \emph{Journal of Vegetation Science}, 14: 681-692.\cr\cr
 Lotwick, H.W. & Silverman, B.W. 1982. Methods for analysing spatial processes of several types of points. \emph{Journal of the Royal Statistical Society B}, 44:403-413.\cr\cr
 Ripley B.D. 1977. Modelling spatial patterns. \emph{Journal of the Royal Statistical Society B}, 39:172-192.\cr\cr
 Wiegand, T. & Moloney, K.A. 2004. Rings, circles, and null-models for point pattern analysis in ecology. \emph{Oikos}, 104:209-229.

--- a/man/k12val.Rd
+++ b/man/k12val.Rd
@@ -22,7 +22,7 @@
 \details{
  Function \code{K12val} returns individual values of \emph{K12(r)} and associated functions (see \code{\link{k12fun}})
 estimated at each type 1 point of the pattern. For a given distance \emph{r}, these values can be mapped within the sampling window, as in
- Getis & Franklin 1987 or Plissier & Goreaud 2001. 
+ Getis & Franklin 1987 or P?Pelissier & Goreaud 2001. 
 }
 \value{
 A list of class \code{c("vads","k12val")} with essentially the following components:
@@ -35,7 +35,7 @@
 }
 \references{ 
 Getis, A. and Franklin, J. 1987. Second-order neighborhood analysis of mapped point patterns. \emph{Ecology}, 68:473-477.\cr\cr
-  Plissier, R. and Goreaud, F. 2001. A practical approach to the study of spatial structure in simple cases of heterogeneous vegetation. \emph{Journal of Vegetation Science}, 12:99-108.
+  P?Pelissier, R. and Goreaud, F. 2001. A practical approach to the study of spatial structure in simple cases of heterogeneous vegetation. \emph{Journal of Vegetation Science}, 12:99-108.
 }
 \author{
 	\email{Raphael.Pelissier@ird.fr}

--- a/man/kdfun.Rd
+++ b/man/kdfun.Rd
 \encoding{latin1}
 \name{kdfun}
 \alias{kdfun}
-\title{Multiscale second-order neigbourhood analysis of a spatial phylogenetic or functional community pattern from fully mapped data}
+\title{Multiscale second-order neighbourhood analysis of a spatial phylogenetic or functional community pattern from fully mapped data}
 \description{
  Computes distance-dependent estimates of Shen et al. (2014) phylogenetic or functional mark correlation functions from a multivariate spatial point pattern 
  in a simple (rectangular or circular) or complex sampling window. Computes optionally local confidence limits of the functions
@@ -21,13 +21,13 @@ kdfun(p, upto, by, dis, nsim=0, alpha = 0.01)
 }
 \details{
 Function \code{kdfun} computes Shen et al. (2014) \eqn{Kd} and \emph{gd}-functions. For a multivariate point pattern consisting of \eqn{S} species with intensity \eqn{\lambda}p, such functions can be estimated from the bivariate \eqn{Kpq}-functions between each pair of different species \eqn{p} and \eqn{q}.
- Function \code{kdfun} is thus a simple wrapper of \code{\link{k12fun}} (Plissier & Goreaud 2014):
+ Function \code{kdfun} is thus a simple wrapper of \code{\link{k12fun}} (P?Pelissier & Goreaud 2014):

  \eqn{Kd(r) = D * Kr(r) / HD * Ks(r) = D * sum(\lambda p * \lambda q * Kpq(r) * dpq) / HD * sum(\lambda p * \lambda q * Kpq(r))}.\cr
  \eqn{gd(r) = D * g(r) / HD * gs(r) = D * sum(\lambda p * \lambda q * gpq(r) * dpq) / HD * sum(\lambda p * \lambda q * gpq(r))}.\cr\cr

  where \eqn{Ks(r)} and \eqn{gs(r)} are distance-dependent versions of Simpson's diversity index, \eqn{D} (see \code{\link{ksfun}}), \eqn{Kr(r)} and \eqn{gr(r)} are distance-dependent versions of Rao's diversity coefficient (see \code{\link{krfun}}); 
-  \eqn{dpq} is the distance between species \eqn{p} and \eqn{q} defined by matrix \code{dis}, typically a taxonomic, phylogentic or functional distance. The advantage here is that as the edge effects vanish between \eqn{Kr(r)} and \eqn{Ks(r)},
+  \eqn{dpq} is the distance between species \eqn{p} and \eqn{q} defined by matrix \code{dis}, typically a taxonomic, phylogenetic or functional distance. The advantage here is that as the edge effects vanish between \eqn{Kr(r)} and \eqn{Ks(r)},
   implementation is fast for a sampling window of any shape. \eqn{Kd(r)} provides the expected phylogenetic or functional distance of two heterospecific individuals a distance less than \emph{r} apart (Shen et al. 2014), while \eqn{gd(r)} 
   provides the same within an annuli between two consecutive distances of \emph{r} and \emph{r-by}.
   
@@ -50,7 +50,7 @@ kdfun(p, upto, by, dis, nsim=0, alpha = 0.01)
 \references{
 Shen, G., Wiegand, T., Mi, X. & He, F. (2014). Quantifying spatial phylogenetic structures of fully stem-mapped plant communities. \emph{Methods in Ecology and Evolution}, 4, 1132-1141.
 
- Plissier, R. & Goreaud, F. ads package for R: A fast unbiased implementation of the K-function family for studying spatial point patterns in irregular-shaped sampling windows. \emph{Journal of Statistical Software}, in press.
+ P?Pelissier, R. & Goreaud, F. ads package for R: A fast unbiased implementation of the K-function family for studying spatial point patterns in irregular-shaped sampling windows. \emph{Journal of Statistical Software}, in press.
 
 }
 \author{\email{Raphael.Pelissier@ird.fr}}

--- a/man/kfun.Rd
+++ b/man/kfun.Rd
 \encoding{latin1}
 \name{kfun}
 \alias{kfun}
-\title{Multiscale second-order neigbourhood analysis of an univariate spatial point pattern}
+\title{Multiscale second-order neighbourhood analysis of an univariate spatial point pattern}
 \description{
-  Computes estimates of Ripley's \emph{K}-function and associated neigbourhood functions from an univariate spatial point pattern 
+  Computes estimates of Ripley's \emph{K}-function and associated neighbourhood functions from an univariate spatial point pattern 
  in a simple (rectangular or circular) or complex sampling window. Computes optionally local confidence limits of the functions
  under the null hypothesis of Complete Spatial Randomness (see Details).
 }
@@ -33,7 +33,7 @@ kfun(p, upto, by, nsim=0, prec=0.01, alpha=0.01)
  \eqn{O(r) = \lambda*g(r)}.\cr\cr
  
  The program introduces an edge effect correction term according to the method proposed by Ripley (1977)
-  and extended to circular and complex sampling windows by Goreaud & Pélissier (1999).\cr\cr
+  and extended to circular and complex sampling windows by Goreaud & P?Pelissier (1999).\cr\cr
 
  Theoretical values under the null hypothesis of CSR as well as
  local Monte Carlo confidence limits and p-values of departure from CSR (Besag & Diggle 1977) are estimated at each distance \eqn{r}.
@@ -57,7 +57,7 @@ kfun(p, upto, by, nsim=0, prec=0.01, alpha=0.01)
  
 Besag J.E. & Diggle P.J. 1977. Simple Monte Carlo tests spatial patterns. \emph{Applied Statistics}, 26:327-333.
  
- Goreaud F. & Pélissier R. 1999. On explicit formulas of edge effect correction for Ripley's K-function. \emph{Journal of Vegetation Science}, 10:433-438.
+ Goreaud F. & P?Pelissier R. 1999. On explicit formulas of edge effect correction for Ripley's K-function. \emph{Journal of Vegetation Science}, 10:433-438.

 Ripley B.D. 1977. Modelling spatial patterns. \emph{Journal of the Royal Statistical Society B}, 39:172-192.
  

--- a/man/kmfun.Rd
+++ b/man/kmfun.Rd
 \encoding{latin1}
 \name{kmfun}
 \alias{kmfun}
-\title{Multiscale second-order neigbourhood analysis of a marked spatial point pattern}
+\title{Multiscale second-order neighbourhood analysis of a marked spatial point pattern}
 \description{
-   Computes estimates of the mark correlation \emph{Km}-function and associated neigbourhood functions from a marked spatial point pattern 
+   Computes estimates of the mark correlation \emph{Km}-function and associated neighbourhood functions from a marked spatial point pattern 
  in a simple (rectangular or circular) or complex sampling window. Computes optionally local confidence limits of the functions
  under the null hypothesis of no correlation between marks (see Details).
 }
@@ -30,7 +30,7 @@ kmfun(p, upto, by, nsim=0, alpha=0.01)
 
 \eqn{gm(r)} is the derivative of \eqn{Km(r)} or pair mark correlation function, which gives the correlation of marks within an annuli between two successive circles with radii \eqn{r} and \eqn{r-by}).\cr\cr
  
-  The program introduces an edge effect correction term according to the method proposed by Ripley (1977) and extended to circular and complex sampling windows by Goreaud & Pélissier (1999).
+  The program introduces an edge effect correction term according to the method proposed by Ripley (1977) and extended to circular and complex sampling windows by Goreaud & P?Pelissier (1999).
  
  Local Monte Carlo confidence limits and p-values of departure from the null hypothesis of no correlation are estimated at each distance \eqn{r}, after reallocating at random the values of \emph{X} over all points of the pattern, the location of trees being kept unchanged.
 }
@@ -50,11 +50,11 @@ kmfun(p, upto, by, nsim=0, alpha=0.01)
 Applications of this function can be found in Oddou-Muratorio \emph{et al.} (2004) and Madelaine \emph{et al.} (submitted).
 }

-  \references{Goreaud, F. 2000. \emph{Apports de l'analyse de la structure spatiale en foret tempérée à l'étude et la modélisation des peuplements complexes}. Thèse de doctorat, ENGREF, Nancy, France.\cr\cr
-Goreaud F. & Pélissier R. 1999. On explicit formulas of edge effect correction for Ripley's K-function. \emph{Journal of Vegetation Science}, 10:433-438.\cr\cr
-Madelaine, C., Pélissier, R., Vincent, G., Molino, J.-F., Sabatier, D., Prévost, M.-F. & de Namur, C. 2007. Mortality and recruitment in a lowland tropical rainforest of French Guiana: effects of soil type and species guild. \emph{Journal of Tropical Ecology}, 23:277-287.
+  \references{Goreaud, F. 2000. \emph{Apports de l'analyse de la structure spatiale en foret tempere a l'etude et la modelisation des peuplements complexes}. These de doctorat, ENGREF, Nancy, France.\cr\cr
+Goreaud F. & P?Pelissier R. 1999. On explicit formulas of edge effect correction for Ripley's K-function. \emph{Journal of Vegetation Science}, 10:433-438.\cr\cr
+Madelaine, C., Pelissier, R., Vincent, G., Molino, J.-F., Sabatier, D., Prevost, M.-F. & de Namur, C. 2007. Mortality and recruitment in a lowland tropical rainforest of French Guiana: effects of soil type and species guild. \emph{Journal of Tropical Ecology}, 23:277-287.

-Oddou-Muratorio, S., Demesure-Musch, B., Pélissier, R. & Gouyon, P.-H. 2004. Impacts of gene flow and logging history on the local genetic structure of a scattered tree species, Sorbus torminalis L. \emph{Molecular Ecology}, 13:3689-3702.
+Oddou-Muratorio, S., Demesure-Musch, B., Pelissier, R. & Gouyon, P.-H. 2004. Impacts of gene flow and logging history on the local genetic structure of a scattered tree species, Sorbus torminalis L. \emph{Molecular Ecology}, 13:3689-3702.

 Ripley B.D. 1977. Modelling spatial patterns. \emph{Journal of the Royal Statistical Society B}, 39:172-192.
 }

--- a/man/kp.fun.Rd
+++ b/man/kp.fun.Rd
@@ -2,7 +2,7 @@
 \name{kp.fun}
 \alias{kp.fun}
 \alias{ki.fun}
-\title{ Multiscale second-order neigbourhood analysis of a multivariate spatial point pattern}
+\title{ Multiscale second-order neighbourhood analysis of a multivariate spatial point pattern}
 \description{
 (Formerly \code{ki.fun}) Computes a set of \emph{K12}-functions between all possible marks \eqn{p} and the other marks in
 a multivariate spatial point pattern defined in a simple (rectangular or circular) 

--- a/man/kpqfun.Rd
+++ b/man/kpqfun.Rd
@@ -2,7 +2,7 @@
 \name{kpqfun}
 \alias{kpqfun}
 \alias{kijfun}
-\title{Multiscale second-order neigbourhood analysis of a multivariate spatial point pattern}
+\title{Multiscale second-order neighbourhood analysis of a multivariate spatial point pattern}
 \description{
  (Formerly \code{kijfun}) Computes a set of \emph{K}- and \emph{K12}-functions for all possible pairs of marks \eqn{(p,q)} in a multivariate spatial 
  point pattern defined in a simple (rectangular or circular) 

--- a/man/krfun.Rd
+++ b/man/krfun.Rd
 \encoding{latin1}
 \name{krfun}
 \alias{krfun}
-\title{Multiscale second-order neigbourhood analysis of a multivariate spatial point pattern using Rao quandratic entropy}
+\title{Multiscale second-order neighbourhood analysis of a multivariate spatial point pattern using Rao quadratic entropy}
 \description{
  Computes distance-dependent estimates of Rao's quadratic entropy from a multivariate spatial point pattern 
  in a simple (rectangular or circular) or complex sampling window. Computes optionally local confidence limits of the functions
@@ -23,15 +23,15 @@ krfun(p, upto, by, nsim=0, dis = NULL, H0 = c("rl", "se"), alpha = 0.01)
 \details{
 Function \code{krfun} computes distance-dependent functions of Rao (1982) quadratic entropy (see \code{\link{divc}} in package \code{ade4}).\cr\cr
 For a multivariate point pattern consisting of \eqn{S} species with intensity \eqn{\lambda}p, such functions can be estimated from the bivariate \eqn{Kpq}-functions between each pair of different species \eqn{p} and \eqn{q}.
- Function \code{krfun} is thus a simple wrapper function of \code{\link{k12fun}} and \code{\link{kfun}}, standardized by Rao diversity coefficient (Pélissier & Goreaud 2014):
+ Function \code{krfun} is thus a simple wrapper function of \code{\link{k12fun}} and \code{\link{kfun}}, standardized by Rao diversity coefficient (Pelissier & Goreaud 2014):

  \eqn{Kr(r) = sum(\lambda p * \lambda q * Kpq(r)*dpq) / (\lambda * \lambda * K(r) * HD)}.\cr
  \eqn{gr(r) = sum(\lambda p * \lambda q * gpq(r)*dpq) / (\lambda * \lambda * g(r) * HD)}.\cr\cr

-  where \eqn{dpq} is the distance between species \eqn{p} and \eqn{q} defined by matrix \code{dis}, typically a taxonomic, phylogentic or functional distance, and \eqn{HD=sum(Np*Nq*dpq/(N(N - 1)))} is the unbiased version of Rao diversity coefficient (see Shimatani 2001). When \code{dis = NULL}, species are considered each other equidistant and \code{krfun} returns the same results than \code{\link{ksfun}}. 
+  where \eqn{dpq} is the distance between species \eqn{p} and \eqn{q} defined by matrix \code{dis}, typically a taxonomic, phylogenetic or functional distance, and \eqn{HD=sum(Np*Nq*dpq/(N(N - 1)))} is the unbiased version of Rao diversity coefficient (see Shimatani 2001). When \code{dis = NULL}, species are considered each other equidistant and \code{krfun} returns the same results than \code{\link{ksfun}}. 

 The program introduces an edge effect correction term according to the method proposed by Ripley (1977)
-  and extended to circular and complex sampling windows by Goreaud & Pélissier (1999).\cr\cr
+  and extended to circular and complex sampling windows by Goreaud & Pelissier (1999).\cr\cr
 
 Theoretical values under the null hypothesis of either random labelling or species equivalence as well as local Monte Carlo confidence limits and p-values of departure from the null hypothesis (Besag & Diggle 1977) are estimated at each distance \eqn{r}.

@@ -56,11 +56,11 @@ The species equivalence hypothesis (H0 = "se") is tested by randomizing the betw

 Shimatani, K. 2001. On the measurement of species diversity incorporating species differences. \emph{Oikos}, 93, 135-147.
 
- Goreaud F. & Pélissier R. 1999. On explicit formulas of edge effect correction for Ripley's K-function. \emph{Journal of Vegetation Science}, 10:433-438.
+ Goreaud F. & Pelissier R. 1999. On explicit formulas of edge effect correction for Ripley's K-function. \emph{Journal of Vegetation Science}, 10:433-438.

 Ripley B.D. 1977. Modelling spatial patterns. \emph{Journal of the Royal Statistical Society B}, 39:172-192.
 
- Pélissier, R. & Goreaud, F. 2014. ads package for R: A fast unbiased implementation of the k-function family for studying spatial point patterns in irregular-shaped sampling windows. \emph{Journal of Statistical Software}, in press.
+ Pelissier, R. & Goreaud, F. 2014. ads package for R: A fast unbiased implementation of the k-function family for studying spatial point patterns in irregular-shaped sampling windows. \emph{Journal of Statistical Software}, in press.
 
 }
 \author{\email{Raphael.Pelissier@ird.fr}}

--- a/man/ksfun.Rd
+++ b/man/ksfun.Rd
 \encoding{latin1}
 \name{ksfun}
 \alias{ksfun}
-\title{Multiscale second-order neigbourhood analysis of a multivariate spatial point pattern using Simpson diversity}
+\title{Multiscale second-order neighbourhood analysis of a multivariate spatial point pattern using Simpson diversity}
 \description{
  Computes estimates of Shimatani \emph{alpha} and \emph{beta} functions of Simpson diversity from a multivariate spatial point pattern 
  in a simple (rectangular or circular) or complex sampling window. Computes optionally local confidence limits of the functions
@@ -21,18 +21,18 @@ ksfun(p, upto, by, nsim=0, alpha=0.01)
 \details{
 Function \code{ksfun} computes Shimatani \eqn{\alpha(r)} and \eqn{\beta(r)} functions of Simpson diversity, called here \eqn{Ks(r)} and \eqn{gs(r)}, respectively.\cr\cr
 For a multivariate point pattern consisting of \eqn{S} species with intensity \eqn{\lambda}p, Shimatani (2001) showed that
-  a distance-dependent measure of Simpson (1949) diversity can be estimated from Ripley (1977) \eqn{K}-function computed for each species separately and for all the points grouped toghether (see also Eckel et al. 2008).
+  a distance-dependent measure of Simpson (1949) diversity can be estimated from Ripley (1977) \eqn{K}-function computed for each species separately and for all the points grouped together (see also Eckel et al. 2008).
  Function \code{ksfun} is thus a simple wrapper function of \code{\link{kfun}}, standardized by Simpson diversity coefficient:
  
  
  \eqn{Ks(r) = 1 - sum(\lambda p * \lambda p * Kp(r)) / (\lambda * \lambda * K(r) * D)} which is a standardized estimator of \eqn{\alpha(r)} in Shimatani (2001).\cr\cr
  \eqn{gs(r) = 1 - sum(\lambda p * \lambda p * gp(r)) / (\lambda * \lambda * g(r) * D)} corresponding to a standardized version of \eqn{\beta(r)} in Shimatani (2001).\cr\cr
  
-  \eqn{Kp(r)} and \eqn{K(r)} (resp. \eqn{gp(r)} and \eqn{g(r)}) are univariate K-functions computed for species \eqn{p} and for all species toghether; \eqn{D = 1 - sum(Np * (Np - 1) / (N*(N - 1)))} is the unbiased version of Simpson diversity,
+  \eqn{Kp(r)} and \eqn{K(r)} (resp. \eqn{gp(r)} and \eqn{g(r)}) are univariate K-functions computed for species \eqn{p} and for all species together; \eqn{D = 1 - sum(Np * (Np - 1) / (N*(N - 1)))} is the unbiased version of Simpson diversity,
  with \eqn{Np} the number of individuals of species \eqn{p} in the sample and \eqn{N = sum(Np)}.

  The program introduces an edge effect correction term according to the method proposed by Ripley (1977)
-  and extended to circular and complex sampling windows by Goreaud & Pélissier (1999).\cr\cr
+  and extended to circular and complex sampling windows by Goreaud & P?Pelissier (1999).\cr\cr
 
  The theoretical values of \eqn{gr(r)} and \eqn{Kr(r)} under the null hypothesis of random labelling is 1 for all \eqn{r}.
  Local Monte Carlo confidence limits and p-values of departure from this hypothesis are estimated at each distance \eqn{r} by reallocating at random the species labels among points of the pattern, keeping the point locations unchanged.
@@ -46,17 +46,17 @@ ksfun(p, upto, by, nsim=0, alpha=0.01)
 \item{obs }{a vector of estimated values for the observed point pattern.}
 \item{theo }{a vector of theoretical values expected under the null hypothesis of random labelling, i.e. 1 for all \eqn{r}.}
 \item{sup }{(optional) if \code{nsim>0} a vector of the upper local confidence limits of a random distribution of species labels at a significant level \eqn{\alpha}.}
- \item{inf }{(optional) if \code{nsim>0} a vector of the lower local confidence limits of a Prandom distribution of species labels at a significant level \eqn{\alpha}.}
+ \item{inf }{(optional) if \code{nsim>0} a vector of the lower local confidence limits of a random distribution of species labels at a significant level \eqn{\alpha}.}
 \item{pval }{(optional) if \code{nsim>0} a vector of local p-values of departure from a random distribution of species labels.}
 }
 \references{
- Shimatani K. 2001. Multivariate point processes and spatial variation in species diversity. \emph{Forest Ecology and Managaement}, 142:215-229.
+ Shimatani K. 2001. Multivariate point processes and spatial variation in species diversity. \emph{Forest Ecology and Management}, 142:215-229.

 Eckel, S., Fleisher, F., Grabarnik, P. and Schmidt V. 2008. An investigation of the spatial correlations for relative purchasing power in Baden-Wurttemberg. \emph{AstA - Advances in Statistical Analysis}, 92:135-152.
 
 Simpson, E.H. 1949. Measurement of diversity. \emph{Nature}, 688:163.
 
- Goreaud F. & Pélissier R. 1999. On explicit formulas of edge effect correction for Ripley's K-function. \emph{Journal of Vegetation Science}, 10:433-438.
+ Goreaud F. & P?Pelissier R. 1999. On explicit formulas of edge effect correction for Ripley's K-function. \emph{Journal of Vegetation Science}, 10:433-438.

 Ripley B.D. 1977. Modelling spatial patterns. \emph{Journal of the Royal Statistical Society B}, 39:172-192.
 }

--- a/man/kval.Rd
+++ b/man/kval.Rd
@@ -18,9 +18,9 @@
  \item{by }{interval length between successive sample circles radii (see Details).}
 }
 \details{
- Function \code{kval} returns indivdiual values of \emph{K(r)} and associated functions (see \code{\link{kfun}})
+ Function \code{kval} returns individual values of \emph{K(r)} and associated functions (see \code{\link{kfun}})
 estimated for each point of the pattern. For a given distance \emph{r}, these values can be mapped within the sampling window 
- (Getis & Franklin 1987, Pélissier & Goreaud 2001).  
+ (Getis & Franklin 1987, P?Pelissier & Goreaud 2001).  
 }
 \value{
 A list of class \code{c("vads","kval")} with essentially the following components:
@@ -33,7 +33,7 @@ A list of class \code{c("vads","kval")} with essentially the following component
 }
 \references{ 
  Getis, A. and Franklin, J. 1987. Second-order neighborhood analysis of mapped point patterns. \emph{Ecology}, 68:473-477.\cr\cr
-  Pélissier, R. and Goreaud, F. 2001. A practical approach to the study of spatial structure in simple cases of heterogeneous vegetation. \emph{Journal of Vegetation Science}, 12:99-108.
+  P?Pelissier, R. and Goreaud, F. 2001. A practical approach to the study of spatial structure in simple cases of heterogeneous vegetation. \emph{Journal of Vegetation Science}, 12:99-108.
 }
 \author{
 	\email{Raphael.Pelissier@ird.fr}

--- a/man/mimetic.Rd
+++ b/man/mimetic.Rd
@@ -13,14 +13,14 @@ mimetic(x,upto=NULL,by=NULL,prec=NULL,nsimax=3000,conv=50)
  \item{x }{either a \code{("fads", "kfun")} object or a \code{"spp"} object of type "univariate" defining a spatial point pattern in a given sampling window (see \code{\link{kfun}} or \code{\link{spp}}).}
  \item{upto }{(optional) maximum radius of the sample circles when \code{x} is a \code{"spp"} object.}
  \item{by }{(optional) interval length between successive sample circles radii when \code{x} is a \code{"spp"} object.}
-  \item{prec }{precision of point coordinates generated during simulations when \code{x} is a \code{"spp"} object. By default prec=0.01 or the value used in fonction \code{kfun} when \code{x} is a \code{("fads", "kfun")} object.}
+  \item{prec }{precision of point coordinates generated during simulations when \code{x} is a \code{"spp"} object. By default prec=0.01 or the value used in function \code{kfun} when \code{x} is a \code{("fads", "kfun")} object.}
  \item{nsimax }{maximum number of simulations allowed. By default the process stops after \code{nsimax=3000} if convergence is not reached.}
  \item{conv }{maximum number of simulations without optimization gain (convergence criterion).}
 }
 \details{
 Function \code{mimetic} uses a stepwise depletion-replacement algorithm to generate a point pattern whose L-function is optimized with regards to an observed one, following the mimetic point process principle (Goreaud et al. 2004).  
- Four points are randomly deleted at each step of the process and replaced by new points that minimize the following cost function:||\eqn{Lobs(r) - Lsim (r)}||)^2. The simulation stops as soon as the cost fonction doesn't decrease 
- after \code{conv} simulations or after a maximum of \code{nsimax} simulations. The process apply to rectangular, circular or comlex sampling windows (see \code{\link{spp}}). There exist a \code{plot} method that displays diagnostic 
+ Four points are randomly deleted at each step of the process and replaced by new points that minimize the following cost function:||\eqn{Lobs(r) - Lsim (r)}||)^2. The simulation stops as soon as the cost function doesn't decrease 
+ after \code{conv} simulations or after a maximum of \code{nsimax} simulations. The process apply to rectangular, circular or complex sampling windows (see \code{\link{spp}}). There exist a \code{plot} method that displays diagnostic 
 plots, i.e. the observed and simulated L-function, the simulated point pattern and the successive values of the cost function. 
 }
 \value{

--- a/man/plot.fads.Rd
+++ b/man/plot.fads.Rd
@@ -10,9 +10,9 @@
 \alias{plot.fads.krfun}
 \alias{plot.fads.kdfun}
 \alias{plot.fads.mimetic}
-\title{Plot second-order neigbourhood functions}
+\title{Plot second-order neighbourhood functions}
 \description{
- Plot second-order neigbourhood function estimates returned by functions \code{\link{kfun}, \link{k12fun}, \link{kmfun}}, \cr
+ Plot second-order neighbourhood function estimates returned by functions \code{\link{kfun}, \link{k12fun}, \link{kmfun}}, \cr
 	\code{ \link{kijfun} or \link{ki.fun}}.
 }
 \usage{
@@ -20,9 +20,9 @@
 }
 \arguments{
  \item{x}{an object of class \code{"fads"} (see Details).}
-  \item{opt}{one of \code{c("all","L","K","n","g")} to dislay either all or one of the functions in a single window. By default \code{opt = "all"} for \code{fads} 
+  \item{opt}{one of \code{c("all","L","K","n","g")} to display either all or one of the functions in a single window. By default \code{opt = "all"} for \code{fads} 
  objects of subclass \code{"kfun"}, \code{"k12fun"}, or \code{"kmfun"};  by default \code{opt = "L"} for \code{fads} objects of subclass \code{"kij"}, or \code{"ki."}.}
-  \item{cols}{(optional) coulours used for plotting functions.}
+  \item{cols}{(optional) colours used for plotting functions.}
  \item{lty}{(optional) line types used for plotting functions.}
  \item{main}{by default, the value of argument x, otherwise a text to be displayed as a title of the plot. \code{main=NULL} displays no title.}
  \item{sub}{by default, the name of the function displayed, otherwise a text to be displayed as function subtitle. \code{sub=NULL} displays no subtitle.}

--- a/man/plot.spp.Rd
+++ b/man/plot.spp.Rd
@@ -15,9 +15,9 @@ maxsize, scale=TRUE, add=FALSE, legend=TRUE, csize=1, ...)
  \item{out}{by default \code{out = FALSE}. If \code{TRUE} points of the pattern located outside the sampling window are plotted.}
  \item{use.marks}{by default \code{use.marks = TRUE}. If \code{FALSE} different symbols are not used for each mark of multivariate
   or marked point patterns, so that they are plotted as univariate (see \code{\link{spp}}).}
-  \item{cols}{(optional) the coulour(s) used to plot points located inside the sampling window (see Details).}
+  \item{cols}{(optional) the colour(s) used to plot points located inside the sampling window (see Details).}
  \item{chars}{(optional) plotting character(s) used to plot points located inside the sampling window (see Details).}
-  \item{cols.out}{(optional) if \code{out = TRUE}, the coulour(s) used to plot points located outside the sampling window (see Details).}
+  \item{cols.out}{(optional) if \code{out = TRUE}, the colour(s) used to plot points located outside the sampling window (see Details).}
  \item{chars.out}{(optional) if \code{out = TRUE}, plotting character(s) used to plot points located outside the sampling window (see Details).}
  \item{maxsize}{(optional) maximum size of plotting symbols. By default \code{maxsize} is automatically adjusted to plot size.}
  \item{csize}{scaling factor for font size so that actual font size is \code{par("cex")*csize}. By default \code{csize = 1}.}
@@ -45,11 +45,11 @@ Then the points themselves are plotted, in a fashion that depends on the type of
 	
 	Arguments \code{cols} and \code{cols.out} (if \code{out = TRUE}) determine the colour(s) used to display the points located inside and outside the sampling window, respectively.
 	Colours may be specified as codes or colour names (see \code{\link[graphics]{par}("col")}). For univariate and marked point patterns, \code{cols} and \code{cols.out} are single character strings, while 
-	for multivariate point patterns they are charcater vectors of same length as \code{levels(x$marks)} and \code{levels(x$marksout)}, respectively.
+	for multivariate point patterns they are character vectors of same length as \code{levels(x$marks)} and \code{levels(x$marksout)}, respectively.

 	Arguments \code{chars} and \code{chars.out} (if \code{out = TRUE}) determine the symbol(s) used to display the points located inside and outside the sampling window, respectively.
 	Symbols may be specified as codes or character strings (see \code{\link[graphics]{par}("pch")}). For univariate point patterns, \code{chars} and \code{chars.out} are single character strings, while 
-	for multivariate point patterns they are charcater vectors of same length as \code{levels(x$marks)} and \code{levels(x$marksout)}, respectively. For marked point patterns, 
+	for multivariate point patterns they are character vectors of same length as \code{levels(x$marks)} and \code{levels(x$marksout)}, respectively. For marked point patterns, 
 	\code{chars} and \code{chars.out} can only take the value \code{"circles"} or \code{"squares"}.	
 }
 \value{