Title: Procrustes Application to Cophylogenetic Analysis
Description: Procrustes analyses to infer co-phylogenetic
matching between pairs of (ultrametric) phylogenetic trees.
Author: Juan Antonio Balbuena <ja.balbuena@uv.es>, Timothee Poisot

Author: Juan Antonio Balbuena <j.a.balbuena@uv.es>, Timothee Poisot

Note: The current version (0.4.1) fixes an issue in the add_pcoord
function where negative eigenvalues were left uncorrected when
the user specified that a correction be applied.

matching between pairs of phylogenetic trees.
Fernando Cagua <fernando@cagua.co>; see PLoS ONE Balbuena et al 2013 <https://doi.org/10.1371/journal.pone.0061048>
Note: The current version (0.4.2) fixes a numerical issue with
symmetric implementation of the paco_links function.

Title: iPACOSE, PACOSE and other methods for covariance selection
Depends: ggm, igraph, ppls, MASS, gRbase, corpcor, glmnet, mvtnorm
Author: Vincent Guillemot, Andreas Bender
Description: Implementation of iPACOSE, PACOSE and of various methods
for the estimation of covariance/precision matrices under the
constraint that some partial correlation coefficients are equal

Depends: ggm, igraph0, ppls, MASS, gRbase, corpcor, glmnet, mvtnorm

Depends: ggm, igraph, ppls, MASS, corpcor, glmnet, mvtnorm, lcd

Title: Testing for Partial Copulas and the Simplifying Assumption in
Authors@R: person("Malte S.", "Kurz", email = "malte.kurz@stat.uni-muenchen.de",
Maintainer: Malte S. Kurz <malte.kurz@stat.uni-muenchen.de>
Description: Routines for two different test types, the Equal Correlation (ECORR) test and the Vectorial Independence (VI) test are provided. The tests can be applied to check whether a conditional copula coincides with its partial copula. Functions to test whether a regular vine copula satisfies the so-called simplifying assumption or to test a single copula within a regular vine copula to be a (j-1)-th order partial copula are available. The ECORR test comes with a decision tree approach to allow testing in high-dimensional settings.
Imports: Rcpp (>= 0.11.4), VineCopula (>= 2.0.5), numDeriv, ggplot2(>=