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.

Estimation of 2-level factor copula-based regression models  for clustered data where the response variable can be either discrete or continuous.

Estimation and goodness-of-fit functions for copula-based models of bivariate data with arbitrary distributions  (discrete, continuous, mixture of both types). The copula families considered here are the  Gaussian, Student, Clayton, Frank, Gumbel, Joe, Plackett, BB1, BB6, BB7,BB8, together with the following non-central squared copula families in Nasri (2020) <doi:10.1016/j.spl.2020.108704>: ncs-gaussian, ncs-clayton, ncs-gumbel, ncs-frank, ncs-joe, and ncs-plackett. For theoretical details, see, e.g., Nasri and Remillard (2023) <arXiv:2301.13408>.