[Vinecopula-commits] r135 - in pkg: . R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Do Sep 17 14:24:06 CEST 2015
Author: tnagler
Date: 2015-09-17 14:24:05 +0200 (Thu, 17 Sep 2015)
New Revision: 135
Removed:
pkg/man/RVineStructureSelect2.Rd
Modified:
pkg/DESCRIPTION
pkg/NAMESPACE
pkg/R/RVineMatrix.R
pkg/man/RVineStructureSelect.Rd
pkg/man/RVineTreePlot.Rd
pkg/man/VineCopula-package.Rd
Log:
* remove igraph dependency for the package
* update manual files accordingly
Modified: pkg/DESCRIPTION
===================================================================
--- pkg/DESCRIPTION 2015-09-17 12:09:57 UTC (rev 134)
+++ pkg/DESCRIPTION 2015-09-17 12:24:05 UTC (rev 135)
@@ -6,7 +6,7 @@
Author: Ulf Schepsmeier, Jakob Stoeber, Eike Christian Brechmann, Benedikt Graeler, Thomas Nagler, Tobias Erhardt
Maintainer: Tobias Erhardt <tobias.erhardt at tum.de>
Depends: R (>= 2.11.0)
-Imports: graphics, grDevices, stats, utils, MASS, mvtnorm, igraph (>= 1.0.0), network, methods, copula, ADGofTest, lattice
+Imports: graphics, grDevices, stats, utils, MASS, mvtnorm, network, methods, copula, ADGofTest, lattice
Suggests: CDVine, TSP
Description: Tools for bivariate exploratory data analysis, bivariate copula selection and (vine) tree construction are provided. Vine copula models can be estimated either sequentially or by joint maximum likelihood estimation. Sampling algorithms and plotting methods are included. Data is assumed to lie in the unit hypercube (so-called copula data). For C- and D-vines links to the package 'CDVine' are provided.
License: GPL (>= 2)
Modified: pkg/NAMESPACE
===================================================================
--- pkg/NAMESPACE 2015-09-17 12:09:57 UTC (rev 134)
+++ pkg/NAMESPACE 2015-09-17 12:24:05 UTC (rev 135)
@@ -15,10 +15,6 @@
"qnorm", "qt", "runif", "uniroot", "var")
importFrom("utils", "combn", "getFromNamespace", "modifyList")
importFrom("ADGofTest", "ad.test")
-importFrom("igraph", "E", "E<-", "V", "V<-", "as_adjacency_matrix",
- "as_edgelist", "delete_edges", "ends", "graph_from_adjacency_matrix",
- "graph_from_edgelist", "gsize", "layout_in_circle", "layout_with_graphopt",
- "make_full_graph", "mst", "plot.igraph")
export(pobs)
Modified: pkg/R/RVineMatrix.R
===================================================================
--- pkg/R/RVineMatrix.R 2015-09-17 12:09:57 UTC (rev 134)
+++ pkg/R/RVineMatrix.R 2015-09-17 12:24:05 UTC (rev 135)
@@ -269,81 +269,7 @@
return(M)
}
-as.RVineMatrix <- function(RVine) {
-
- n <- length(RVine$Tree) + 1
- con <- list()
- nam <- V(RVine$Tree[[1]])$name
-
- conditionedSets <- NULL
- corresppondingParams <- list()
- corresppondingTypes <- list()
-
- print(is.list(E(RVine$Tree[[n - 1]])$conditionedSet))
-
- conditionedSets[[n - 1]][[1]] <- (E(RVine$Tree[[n - 1]])$conditionedSet)
- for (k in 1:(n - 2)) {
- conditionedSets[[k]] <- E(RVine$Tree[[k]])$conditionedSet
- corresppondingParams[[k]] <- as.list(E(RVine$Tree[[k]])$Copula.param)
- corresppondingTypes[[k]] <- as.list(E(RVine$Tree[[k]])$Copula.type)
- }
- corresppondingParams[[n - 1]] <- list()
- corresppondingParams[[n - 1]][[1]] <- (E(RVine$Tree[[n - 1]])$Copula.param)
- corresppondingTypes[[n - 1]] <- as.list(E(RVine$Tree[[n - 1]])$Copula.type)
-
- Param <- array(dim = c(n, n))
- Params2 <- array(0, dim = c(n, n))
- Type <- array(dim = c(n, n))
- M <- matrix(NA, n, n)
-
- for (k in 1:(n - 1)) {
- w <- conditionedSets[[n - k]][[1]][1]
-
- M[k, k] <- w
- M[(k + 1), k] <- conditionedSets[[n - k]][[1]][2]
-
- Param[(k + 1), k] <- corresppondingParams[[n - k]][[1]][1]
- Params2[(k + 1), k] <- corresppondingParams[[n - k]][[1]][2]
-
- Type[(k + 1), k] <- corresppondingTypes[[n - k]][[1]]
-
- if (k == (n - 1)) {
- M[(k + 1), (k + 1)] <- conditionedSets[[n - k]][[1]][2]
- } else {
- for (i in (k + 2):n) {
- for (j in 1:length(conditionedSets[[n - i + 1]])) {
- cs <- conditionedSets[[n - i + 1]][[j]]
- if (cs[1] == w) {
- M[i, k] <- cs[2]
- break
- } else if (cs[2] == w) {
- M[i, k] <- cs[1]
- break
- }
- }
- Param[i, k] <- corresppondingParams[[n - i + 1]][[j]][1]
- Params2[i, k] <- corresppondingParams[[n - i + 1]][[j]][2]
- Type[i, k] <- corresppondingTypes[[n - i + 1]][[j]]
-
- conditionedSets[[n - i + 1]][[j]] <- NULL
- corresppondingParams[[n - i + 1]][[j]] <- NULL
- corresppondingTypes[[n - i + 1]][[j]] <- NULL
- }
- }
-
- }
-
- M <- M + 1
- M[is.na(M)] <- 0
- Type[is.na(Type)] <- 0
-
- return(RVineMatrix(M,
- family = Type,
- par = Param,
- par2 = Params2,
- names = nam))
-
-}
+as.RVineMatrix <- function(RVine) as.RVM2(RVine)
###########################################################################
Modified: pkg/man/RVineStructureSelect.Rd
===================================================================
--- pkg/man/RVineStructureSelect.Rd 2015-09-17 12:09:57 UTC (rev 134)
+++ pkg/man/RVineStructureSelect.Rd 2015-09-17 12:24:05 UTC (rev 135)
@@ -1,4 +1,4 @@
-\name{RVineStructureSelect}
+\name{RVineStructureSelect}
\alias{RVineStructureSelect}
\title{Sequential Specification of R- and C-Vine Copula Models}
@@ -10,14 +10,14 @@
\usage{
RVineStructureSelect(data, familyset = NA, type = 0, selectioncrit = "AIC",
- indeptest = FALSE, level = 0.05, trunclevel = NA,
- progress = FALSE, weights = NA, rotations = TRUE)
+ indeptest = FALSE, level = 0.05, trunclevel = NA,
+ progress = FALSE, weights = NA, rotations = TRUE)
}
\arguments{
\item{data}{An N x d data matrix (with uniform margins).}
\item{familyset}{An integer vector of pair-copula families to select from (the independence copula MUST NOT be specified in this vector unless one wants to fit an independence vine!).
- The vector has to include at least one pair-copula family that allows for positive and one that allows for negative dependence. Not listed copula families might be included to better handle limit cases.
+ The vector has to include at least one pair-copula family that allows for positive and one that allows for negative dependence. Not listed copula families might be included to better handle limit cases.
If \code{familyset = NA} (default), selection among all possible families is performed.
Coding of pair-copula families: \cr
\code{1} = Gaussian copula \cr
@@ -25,14 +25,14 @@
\code{3} = Clayton copula \cr
\code{4} = Gumbel copula \cr
\code{5} = Frank copula \cr
- \code{6} = Joe copula \cr
+ \code{6} = Joe copula \cr
\code{7} = BB1 copula \cr
\code{8} = BB6 copula \cr
\code{9} = BB7 copula \cr
\code{10} = BB8 copula \cr
\code{13} = rotated Clayton copula (180 degrees; ``survival Clayton'') \cr
\code{14} = rotated Gumbel copula (180 degrees; ``survival Gumbel'') \cr
- \code{16} = rotated Joe copula (180 degrees; ``survival Joe'') \cr
+ \code{16} = rotated Joe copula (180 degrees; ``survival Joe'') \cr
\code{17} = rotated BB1 copula (180 degrees; ``survival BB1'')\cr
\code{18} = rotated BB6 copula (180 degrees; ``survival BB6'')\cr
\code{19} = rotated BB7 copula (180 degrees; ``survival BB7'')\cr
@@ -112,10 +112,10 @@
Computational Statistics & Data Analysis, 59 (1), 52-69.
}
-\author{Jeffrey Dissmann, Eike Brechmann, Ulf Schepsmeier}
+\author{Jeffrey Dissmann, Eike Brechmann, Ulf Schepsmeier, Thomas Nagler}
\seealso{\code{\link{RVineTreePlot}}, \code{\link{RVineCopSelect}}}
-
+
\examples{
# load data set
data(daxreturns)
@@ -127,7 +127,7 @@
\dontrun{
# specify a C-vine copula model with only Clayton, Gumbel and Frank copulas (time-consuming)
-CVM <- RVineStructureSelect(daxreturns, c(3,4,5), "CVine")
+CVM <- RVineStructureSelect2(daxreturns, c(3,4,5), "CVine")
}
\dontrun{
Deleted: pkg/man/RVineStructureSelect2.Rd
===================================================================
--- pkg/man/RVineStructureSelect2.Rd 2015-09-17 12:09:57 UTC (rev 134)
+++ pkg/man/RVineStructureSelect2.Rd 2015-09-17 12:24:05 UTC (rev 135)
@@ -1,145 +0,0 @@
-\name{RVineStructureSelect2}
-\alias{RVineStructureSelect2}
-
-\title{Sequential Specification of R- and C-Vine Copula Models}
-
-\description{
-This function fits either an R- or a C-vine copula model to a d-dimensional copula data set.
-Tree structures are determined and appropriate pair-copula families are selected using \code{\link{BiCopSelect}} and estimated sequentially (forward selection of trees).
-}
-
-\usage{
-RVineStructureSelect2(data, familyset = NA, type = 0, selectioncrit = "AIC",
- indeptest = FALSE, level = 0.05, trunclevel = NA,
- progress = FALSE, weights = NA, rotations = TRUE)
-}
-
-\arguments{
- \item{data}{An N x d data matrix (with uniform margins).}
- \item{familyset}{An integer vector of pair-copula families to select from (the independence copula MUST NOT be specified in this vector unless one wants to fit an independence vine!).
- The vector has to include at least one pair-copula family that allows for positive and one that allows for negative dependence. Not listed copula families might be included to better handle limit cases.
- If \code{familyset = NA} (default), selection among all possible families is performed.
- Coding of pair-copula families: \cr
- \code{1} = Gaussian copula \cr
- \code{2} = Student t copula (t-copula) \cr
- \code{3} = Clayton copula \cr
- \code{4} = Gumbel copula \cr
- \code{5} = Frank copula \cr
- \code{6} = Joe copula \cr
- \code{7} = BB1 copula \cr
- \code{8} = BB6 copula \cr
- \code{9} = BB7 copula \cr
- \code{10} = BB8 copula \cr
- \code{13} = rotated Clayton copula (180 degrees; ``survival Clayton'') \cr
- \code{14} = rotated Gumbel copula (180 degrees; ``survival Gumbel'') \cr
- \code{16} = rotated Joe copula (180 degrees; ``survival Joe'') \cr
- \code{17} = rotated BB1 copula (180 degrees; ``survival BB1'')\cr
- \code{18} = rotated BB6 copula (180 degrees; ``survival BB6'')\cr
- \code{19} = rotated BB7 copula (180 degrees; ``survival BB7'')\cr
- \code{20} = rotated BB8 copula (180 degrees; ``survival BB8'')\cr
- \code{23} = rotated Clayton copula (90 degrees) \cr
- \code{24} = rotated Gumbel copula (90 degrees) \cr
- \code{26} = rotated Joe copula (90 degrees) \cr
- \code{27} = rotated BB1 copula (90 degrees) \cr
- \code{28} = rotated BB6 copula (90 degrees) \cr
- \code{29} = rotated BB7 copula (90 degrees) \cr
- \code{30} = rotated BB8 copula (90 degrees) \cr
- \code{33} = rotated Clayton copula (270 degrees) \cr
- \code{34} = rotated Gumbel copula (270 degrees) \cr
- \code{36} = rotated Joe copula (270 degrees) \cr
- \code{37} = rotated BB1 copula (270 degrees) \cr
- \code{38} = rotated BB6 copula (270 degrees) \cr
- \code{39} = rotated BB7 copula (270 degrees) \cr
- \code{40} = rotated BB8 copula (270 degrees) \cr
- \code{104} = Tawn type 1 copula \cr
- \code{114} = rotated Tawn type 1 copula (180 degrees) \cr
- \code{124} = rotated Tawn type 1 copula (90 degrees) \cr
- \code{134} = rotated Tawn type 1 copula (270 degrees) \cr
- \code{204} = Tawn type 2 copula \cr
- \code{214} = rotated Tawn type 2 copula (180 degrees) \cr
- \code{224} = rotated Tawn type 2 copula (90 degrees) \cr
- \code{234} = rotated Tawn type 2 copula (270 degrees) \cr
- }
- \item{type}{Type of the vine model to be specified:\cr
- \code{0} or \code{"RVine"} = R-vine (default)\cr
- \code{1} or \code{"CVine"} = C-vine\cr
- C- and D-vine copula models with pre-specified order can be specified using \code{CDVineCopSelect} of the package CDVine.
- Similarly, R-vine copula models with pre-specified tree structure can be specified using \code{\link{RVineCopSelect}}.}
- \item{selectioncrit}{Character indicating the criterion for pair-copula selection. Possible choices: \code{selectioncrit = "AIC"} (default) or \code{"BIC"} (see \code{\link{BiCopSelect}}).}
- \item{indeptest}{Logical; whether a hypothesis test for the independence of \code{u1} and \code{u2} is performed before bivariate copula selection
- (default: \code{indeptest = FALSE}; see \code{\link{BiCopIndTest}}).
- The independence copula is chosen for a (conditional) pair if the null hypothesis of independence cannot be rejected.}
- \item{level}{Numerical; significance level of the independence test (default: \code{level = 0.05}).}
- \item{trunclevel}{Integer; level of truncation.}
- \item{progress}{Logical; whether the tree-wise specification progress is printed (default: \code{progress = FALSE}).}
- \item{weights}{Numerical; weights for each observation (opitional).}
- \item{rotations}{If \code{TRUE}, all rotations of the families in \code{familyset} are included.}
-}
-
-\details{
-R-vine trees are selected using maximum spanning trees with absolute values of pairwise Kendall's taus as weights, i.e.,
-the following optimization problem is solved for each tree:
-\deqn{
-\max \sum_{edges\ e_{ij}\ in\ spanning\ tree} |\hat{\tau}_{ij}|,
-}{
-\max \sum_{edges e_{ij} in spanning tree} |\hat{\tau}_{ij}|,
-}
-where \eqn{\hat{\tau}_{ij}} denote the pairwise empirical Kendall's taus and a spanning tree is a tree on all nodes.
-The setting of the first tree selection step is always a complete graph.
-For subsequent trees, the setting depends on the R-vine construction principles, in particular on the proximity condition.
-
-The root nodes of C-vine trees are determined similarly by identifying the node with strongest dependencies to all other nodes.
-That is we take the node with maximum column sum in the empirical Kendall's tau matrix.
-
-Note that a possible way to determine the order of the nodes in the D-vine is to identify a shortest Hamiltonian path in terms
-of weights \eqn{1-|\tau_{ij}|}.
-This can be established for example using the package TSP.
-Example code is shown below.
-}
-
-\value{
- An \code{\link{RVineMatrix}} object with the selected structure (\code{RVM$Matrix}) and families (\code{RVM$family})
- as well as sequentially estimated parameters stored in \code{RVM$par} and \code{RVM$par2}.
-}
-
-\references{
-Brechmann, E. C., C. Czado, and K. Aas (2012).
-Truncated regular vines in high dimensions with applications to financial data.
-Canadian Journal of Statistics 40 (1), 68-85.
-
-Dissmann, J. F., E. C. Brechmann, C. Czado, and D. Kurowicka (2013).
-Selecting and estimating regular vine copulae and application to financial returns.
-Computational Statistics & Data Analysis, 59 (1), 52-69.
-}
-
-\author{Jeffrey Dissmann, Eike Brechmann, Ulf Schepsmeier}
-
-\seealso{\code{\link{RVineTreePlot}}, \code{\link{RVineCopSelect}}}
-
-\examples{
-# load data set
-data(daxreturns)
-
-# select the R-vine structure, families and parameters
-# using only the first 4 variables and the first 750 observations
-# we allow for the copula families: Gauss, t, Clayton, Gumbel, Frank and Joe
-RVM <- RVineStructureSelect(daxreturns[1:750,1:4], c(1:6), progress = TRUE)
-
-\dontrun{
-# specify a C-vine copula model with only Clayton, Gumbel and Frank copulas (time-consuming)
-CVM <- RVineStructureSelect2(daxreturns, c(3,4,5), "CVine")
-}
-
-\dontrun{
-# determine the order of the nodes in a D-vine using the package TSP (time-consuming)
-library(TSP)
-d <- dim(daxreturns)[2]
-M <- 1 - abs(TauMatrix(daxreturns))
-hamilton <- insert_dummy(TSP(M), label = "cut")
-sol <- solve_TSP(hamilton, method = "repetitive_nn")
-order <- cut_tour(sol, "cut")
-DVM <- D2RVine(order, family = rep(0,d*(d-1)/2), par = rep(0, d*(d-1)/2))
-RVineCopSelect(daxreturns, c(1:6), DVM$Matrix)
-}
-}
-
Modified: pkg/man/RVineTreePlot.Rd
===================================================================
--- pkg/man/RVineTreePlot.Rd 2015-09-17 12:09:57 UTC (rev 134)
+++ pkg/man/RVineTreePlot.Rd 2015-09-17 12:24:05 UTC (rev 135)
@@ -4,105 +4,37 @@
\title{Visualisation of R-Vine Tree Structure}
\description{
-This function plots one or all trees of a given R-vine copula model.
+Function is deprecated since \code{VineCopula 2.0}. Use \code{\link[VineCopula:plot.RVineMatrix]{plot.RVineMatrix}} instead.
}
\usage{
-RVineTreePlot(data = NULL, RVM, method = "mle", max.df = 30,
- max.BB = list(BB1=c(5,6),BB6=c(6,6),BB7=c(5,6),BB8=c(6,1)),
- tree = "ALL", edge.labels = c("family"), P = NULL, legend = FALSE)
+RVineTreePlot(x, tree = "ALL", type = 0, edge.labels = NULL, legend.pos = "bottomleft",
+ interactive = FALSE, ...)
}
\arguments{
- \item{data}{An N x d data matrix (with uniform margins), default: \code{data = NULL}.}
- \item{RVM}{An \code{\link{RVineMatrix}} object including the structure and the pair-copula families and parameters.}
- \item{method}{Character indicating the estimation method:
- either maximum likelihood estimation (\code{method = "mle"}; default) or inversion of Kendall's tau (\code{method = "itau"}).}
- \item{max.df}{Numeric; upper bound for the estimation of the degrees of freedom parameter of the t-copula
- (default: \code{max.df = 30}; for more details see \code{\link{BiCopEst}}).}
- \item{max.BB}{List; upper bounds for the estimation of the two parameters (in absolute values) of the BB1, BB6, BB7 and BB8 copulas \cr
- (default: \code{max.BB = list(BB1=c(5,6),BB6=c(6,6),BB7=c(5,6),BB8=c(6,1))}).}
- \item{tree}{Number of the tree to be plotted or \code{tree = "ALL"} (default) to plot all trees.}
- \item{edge.labels}{Vector of edge labels. Possible choices:\cr
- \code{FALSE}: no edge labels\cr
- \code{"family"}: pair-copula families (default)\cr
- \code{"par"}: pair-copula parameters\cr
- \code{"par2"}: second pair-copula parameters\cr
- \code{"theotau"}: theoretical Kendall's tau values corresponding to pair-copula families and parameters (see \code{\link{BiCopPar2Tau}})\cr
- \code{"emptau"}: empirical Kendall's tau values (only if data is provided!)\cr
- \code{"pair"}: indices of (conditioned) pair of variables identified by the edges}
- \item{P}{A list of matrices with two columns for the x-y-coordinates of the nodes in the plot(s) (optional; default: \code{P = NULL}).}
- \item{legend}{If \code{TRUE} the variables are numbered from 1 to d and a legend is added to the plot(s). Otherwise node and edge lables are based on the variable names (default: \code{legend = FALSE}).}
+ \item{x}{\code{RVineMatrix} object.}
+ \item{tree}{\code{"ALL"} or integer vector; specifies which trees are plotted.}
+ \item{type}{integer; specifies how to make use of variable names: \cr
+ \code{0} = variable names are ignored, \cr
+ \code{1} = variable names are used to annotate vertices, \cr
+ \code{2} = uses numbers in plot and adds a legend for variable names.}
+ \item{edge.labels}{character; either a vector of edge labels
+ or one of the following: \cr
+ \code{"family"} = pair-copula family abbreviation (see \code{\link[VineCopula:BiCopName]{BiCopName}}), \cr
+ \code{"par"} = pair-copula parameters, \cr
+ \code{"tau"} = pair-copula Kendall's tau (by conversion of parameters) \cr
+ \code{"family-par"} = pair-copula family and parameters \cr
+ \code{"family-tau"} = pair-copula family and Kendall's tau.
+ }
+ \item{legend.pos}{the \code{x} argument for \code{\link[graphics:legend]{legend}}.}
+ \item{interactive}{logical; if TRUE, the user is asked to adjust the positioning of
+ vertices with his mouse.}
+ \item{\dots}{
+Arguments passed to \code{\link[network:plot.network]{plot.network}}.}
}
-\note{
-The function computes the positions of the nodes automatically with the Fruchterman-Reingold algorithm (see \code{\link{plot.igraph}}
-for a detailed description).
-If one would like to set the positions manually, one has to specify a list of matrices \code{P} in the argument list.
-A good starting point may be to run the function \code{\link{RVineTreePlot}} and manipulate the returning matrix P.
-If data is provided, the parameters of the R-vine copula model are estimated sequentially using \cr
-\code{\link{RVineSeqEst}}/\code{\link{BiCopEst}}.
-Then the edge width is chosen according to the empirical Kendall's tau values. Otherwise theoretical values are used.
-}
+\author{Thomas Nagler}
-\value{
-A list of matrices \code{P} with two columns for the x-y-coordinates of the nodes in the plot(s).
-}
-
-\author{Eike Brechmann}
-
-\seealso{\code{\link{BiCopName}}}
-
-\examples{
-# define 5-dimensional R-vine tree structure matrix
-Matrix <- c(5, 2, 3, 1, 4,
- 0, 2, 3, 4, 1,
- 0, 0, 3, 4, 1,
- 0, 0, 0, 4, 1,
- 0, 0, 0, 0, 1)
-Matrix <- matrix(Matrix, 5, 5)
-
-# define R-vine pair-copula family matrix
-family <- c(0, 1, 3, 4, 4,
- 0, 0, 3, 4, 1,
- 0, 0, 0, 4, 1,
- 0, 0, 0, 0, 3,
- 0, 0, 0, 0, 0)
-family <- matrix(family, 5, 5)
-
-# define R-vine pair-copula parameter matrix
-par <- c(0, 0.2, 0.9, 1.5, 3.9,
- 0, 0, 1.1, 1.6, 0.9,
- 0, 0, 0, 1.9, 0.5,
- 0, 0, 0, 0, 4.8,
- 0, 0, 0, 0, 0)
-par <- matrix(par, 5, 5)
-
-# define second R-vine pair-copula parameter matrix
-par2 <- matrix(0, 5, 5)
-
-# define RVineMatrix object
-RVM <- RVineMatrix(Matrix = Matrix, family = family,
- par = par, par2 = par2,
- names = c("V1", "V2", "V3", "V4", "V5"))
-
-# set random seed for testing
-set.seed(123)
-
-# plot all trees with pair-copula families and
-# theoretical Kendall's tau values as edge labels
-P <- RVineTreePlot(data = NULL, RVM = RVM, tree = "ALL",
- edge.labels = c("family","theotau"),
- P = NULL)
-
-# manipulate the first matrix of x-y-coordinates
-P[[1]][1,] = P[[1]][1,]*2
-
-# re-set random seed for testing
-set.seed(123)
-# plot only the first tree with new coordinates
-P <- RVineTreePlot(data = NULL, RVM = RVM, tree = 1,
- edge.labels = FALSE, P = P)
-}
-
+\seealso{\code{\link[VineCopula:plot.RVineMatrix]{plot.RVineMatrix}}}
Modified: pkg/man/VineCopula-package.Rd
===================================================================
--- pkg/man/VineCopula-package.Rd 2015-09-17 12:09:57 UTC (rev 134)
+++ pkg/man/VineCopula-package.Rd 2015-09-17 12:24:05 UTC (rev 135)
@@ -83,7 +83,7 @@
Date: \tab 2015-07-30\cr
License: \tab GPL (>=2)\cr
Depends: \tab R (\eqn{\geq 2.11.0}{>= 2.11.0})\cr
-Imports: \tab graphics, grDevices, stats, utils, MASS, mvtnorm, igraph (>= 1.0.0), methods, copula, ADGofTest, lattice\cr
+Imports: \tab graphics, grDevices, stats, utils, MASS, mvtnorm, network, methods, copula, ADGofTest, lattice\cr
Suggests: \tab CDVine, TSP\cr
LazyLoad: \tab yes
}
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