CopulaCenR1.2.4 package

Copula-Based Regression Models for Multivariate Censored Data

AIC.CopulaCenR

the AIC of a CopulaCenR object

BIC.CopulaCenR

the BIC of a CopulaCenR object

coef.CopulaCenR

the coefficient estimates of a CopulaCenR object

CopulaCenR

Copula-based regression models for bivariate censored data

data_sim_copula

Simulate bivariate time-to-event times based on specific copula and ma...

fitted.CopulaCenR

Fitted values from CopulaCenR regression models

ic_par_copula

Copula regression models with parametric margins for bivariate interva...

ic_scmprisk_spTran_copula

Copula regression models with semi-parametric transformation margins f...

ic_spTran_copula

Copula regression models with semiparametric margins for bivariate int...

IRsurv

An information ratio-based goodness-of-fit test for copula models on c...

lines.CopulaCenR

Plotting for CopulaCenR fits

logLik.CopulaCenR

the log-likelihood of a CopulaCenR object

lrt_copula

Likelihood-ratio test for covariate effect(s) in copula models

plot.CopulaCenR

Plotting for CopulaCenR fits

predict.CopulaCenR

Predictions from CopulaCenR regression models

print.CopulaCenR

Printing outputs of a CopulaCenR object

print.summary.CopulaCenR

Print the summary of a CopulaCenR object

rc_par_copula

Copula regression models with parametric margins for bivariate right-c...

rc_scmprisk_Cox

Copula regression models with Cox margins for semi-competing risk data...

rc_scmprisk_sp_copula_pen

Penalized copula regression models with Cox semiparametric margins for...

rc_spCox_copula

Copula regression models with Cox semiparametric margins for bivariate...

score_copula

Generalized score test for covariate effect(s)

summary.CopulaCenR

Summarizing outputs of a CopulaCenR object

tau_copula

Calculate Kendall's tau

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.

  • Maintainer: Tao Sun
  • License: GPL (>= 3)
  • Last published: 2024-11-14