vinecopulib / rvinecopulib
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@@ -64,7 +64,7 @@
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#' (`"gaussian"`, `"clayton"`, `"gumbel"`, `"frank"`, and `"joe"`),
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#'
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#' * `"twopar"` contains the parametric families with two parameters
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#' (`"student"`, `"bb1"`, `"bb6"`, `"bb7"`, and `"bb8"`),
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#' (`"t"`, `"bb1"`, `"bb6"`, `"bb7"`, and `"bb8"`),
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#'
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#' * `"elliptical"` contains the elliptical families,
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#'
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#' * `"BB"` contains the BB families,
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#'
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#' * `"itau"` families for which estimation by Kendall's tau inversion is
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#' available (`"indep"`,`"gaussian"`, `"student"`,`"clayton"`, `"gumbel"`,
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#' available (`"indep"`,`"gaussian"`, `"t"`,`"clayton"`, `"gumbel"`,
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#' `"frank"`, `"joe"`).
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#'
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#' @return

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#' Fitting vine copula models
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#'
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#' Automated fitting and model selection for vine copula models with continuous
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#' or discrete data.
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#' or discrete data. Selection of the structure is performed using the algorithm
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#' of Dissmann et al. (2013).
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#'
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#' @inheritParams bicop
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#' @param family_set a character vector of families; see [bicop()] for
@@ -34,6 +35,9 @@
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#' @details
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#'
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#' ## Discrete variables
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#' The dependence measures used to select trees (default: Kendall's tau) are
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#' corrected for ties (see [wdm::wdm]).
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#'
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#' Let `n` be the number of observations and `d` the number of variables.
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#' When at least one variable is discrete, two types of
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#' "observations" are required in `data`: the first `n  x  d` block
@@ -68,7 +72,13 @@
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#'
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#' @seealso [vinecop()], [dvinecop()], [pvinecop()], [rvinecop()],
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#'   [plot.vinecop()], [contour.vinecop()]
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#' @references
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#' Dissmann, J. F., E. C. Brechmann, C. Czado, and D. Kurowicka (2013).
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#' *Selecting and estimating regular vine copulae and application to
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#' financial returns.* Computational Statistics & Data Analysis, 59 (1),
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#' 52-69.
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#' @export
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#'
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#' @examples
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#' ## simulate dummy data
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#' x <- rnorm(30) * matrix(1, 30, 5) + 0.5 * matrix(rnorm(30 * 5), 30, 5)

@@ -9,6 +9,9 @@
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#' If you want the contour boxes to be perfect squares, the plot height should
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#' be \code{1.25/length(tree)*(d - min(tree))} times the plot width.
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#'
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#' The `plot()` method returns an object that (among other things) contains the
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#' `igraph` representation of the graph; see *Examples*.
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#'
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#' @method plot vinecop_dist
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#'
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#' @aliases contour.vinecop_dist plot.vinecop contour.vinecop
@@ -39,15 +42,19 @@
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#'
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#' @examples
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#' # set up vine copula model
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#' d <- 20
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#' n <- 2e2
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#' u <- matrix(runif(n * d), n, d)
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#' u <- matrix(runif(20 * 10), 20, 10)
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#' vc <- vinecop(u, family = "indep")
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#'
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#' # plot
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#' plot(vc, tree = c(1, 2))
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#' plot(vc, edge_labels = "pair")
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#'
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#' # extract igraph representation
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#' plt <- plot(vc, edge_labels = "family_tau")
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#' igr_obj <- get("g", plt$plot_env)[[1]]
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#' igr_obj  # print object
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#' igraph::E(igr_obj)$name  # extract edge labels
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#'
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#' # set up another vine copula model
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#' pcs <- lapply(1:3, function(j) # pair-copulas in tree j
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#'   lapply(runif(4 - j), function(cor) bicop_dist("gaussian", 0, cor)))
Files Coverage
R 94.08%
inst/include/vinecopulib-wrappers.hpp 98.08%
Project Totals (16 files) 94.48%
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