Estimation and statistical process control are performed under
 copula-based time-series models.
 Available are statistical methods in Long and Emura (2014 JCSA),
 Emura et al. (2017 Commun Stat-Simul) <DOI:10.1080/03610918.2015.1073303>,
 Huang and Emura (2021 Commun Stat-Simul) <DOI:10.1080/03610918.2019.1602647>,
 Lin et al. (2021 Comm Stat-Simul) <DOI:10.1080/03610918.2019.1652318>,
 Sun et al. (2020 JSS Series in Statistics)<DOI:10.1007/978-981-15-4998-4>,
 and Huang and Emura (2021, in revision).

Perform likelihood estimation and corresponding analysis under the copula-based Markov chain model for serially dependent event times with a dependent terminal event. Available are statistical methods in Huang, Wang and Emura (2020, JJSD accepted).

Simulating bivariate survival data from copula models.
 Estimation of the association parameter in copula models.
 Two different ways to estimate the association parameter in copula models are implemented.
 A goodness-of-fit test for a given copula model is implemented.
 See Emura, Lin and Wang (2010) <doi:10.1016/j.csda.2010.03.013> for details.

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.