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
    from Gaussian, if specified.

Copula-based regression models for multivariate censored data, including 
 bivariate right-censored data, bivariate interval-censored data, and right/interval-censored 
 semi-competing risks data. Currently supports Clayton, Gumbel, Frank, Joe, AMH and 
 Copula2 copula models. For marginal models, it supports parametric (Weibull, Loglogistic, 
 Gompertz) and semiparametric (Cox and transformation) models. Includes methods for 
 convenient prediction and plotting. Also provides a bivariate time-to-event simulation 
 function and an information ratio-based goodness-of-fit test for copula. Method details 
 can be found in Sun et.al (2019) Lifetime Data Analysis, Sun et.al (2021) Biostatistics, 
 Sun et.al (2022) Statistical Methods in Medical Research, Sun et.al (2022) Biometrics, and
 Sun et al. (2023+) JRSSC.

Data sets used for copula modeling in addition to those in
 the R package 'copula'.  These include a random subsample from the US National
 Education Longitudinal Study (NELS) of 1988 and nursing home data from
 Wisconsin.

Provides a platform where EDAs (estimation of
    distribution algorithms) based on copulas can be implemented and
    studied. The package offers complete implementations of various
    EDAs based on copulas and vines, a group of well-known
    optimization problems, and utility functions to study the
    performance of the algorithms. Newly developed EDAs can be easily
    integrated into the package by extending an S4 class with generic
    functions for their main components.