Title: Fitting Additive Copula Regression Models for Binary Outcome
Maintainer: Simon Boge Brant <simbrant91@gmail.com>
Description: Additive copula regression for regression
problems with binary outcome via gradient boosting
[Brant, Hobæk Haff (2022); <arXiv:2208.04669>]. The fitting process
includes a specialised model selection algorithm for each component, where
each component is found (by greedy optimisation) among all the D-vines with
only Gaussian pair-copulas of a fixed dimension, as specified by the user.
When the variables and structure have been selected, the algorithm then
re-fits the component where the pair-copula distributions can be different
Author: Simon Boge Brant [aut, cre] (<https://orcid.org/0000-0001-8049-7069>),

Title: Estimation of Distribution Algorithms Based on Copula Theory
Author: Yasser González-Fernández <ygf@icmf.inf.cu> and Marta Soto
Description: This package contains implementations of various classes
of Estimation of Distribution Algorithms (EDAs) based on copula
theory: Copula EDAs and Vine EDAs. In this package, EDAs are
implemented using S4 classes with generic functions for its
main parts: seeding, selection, learning, sampling,
replacement, local optimization, termination, and reporting.
The package also includes the implementation of a group of
well-known optimization test problems and utility functions to
Collate: EDA.R seed.R select.R optimize.R report.R replace.R
terminate.R run.R margins.R functions.R indepRuns.R
Packaged: 2011-05-23 17:37:51 UTC; ygonzalezfernandez

Author: Yasser Gonz�lez-Fern�ndez <ygf@icimaf.cu> and Marta Soto

Title: Estimation of Distribution Algorithms Based on Copulas
Description: This package intends to provide a platform where EDAs
(Estimation of Distribution Algorithms) based on copulas can be
implemented and studied. It contains complete implementations
of various EDAs based on copulas and vines and also offers the
possibility of implementing new algorithms that can be
integrated into the package. EDAs are implemented using S4
classes with generic functions for its main parts: seeding,
selection, learning, sampling, replacement, local optimization,
termination, and reporting. The package also provides a group
of well-known benchmark problems and utility functions to study
Collate: EDA.R edaSeed.R edaSelect.R edaOptimize.R edaReport.R
edaReplace.R edaTerminate.R edaRun.R margins.R problems.R