c(person("Yu-Kwang", "Kim", role = c("aut", "cre", "com"), email = "lumiere_profuse@naver.com"),
person("Yu-Hyeong", "Jang", role = c("aut"), email = "jyhmaru@naver.com"),
person("Jae Ho", "Chang", role = c("aut"), email = "chang.2090@buckeyemail.osu.eduwk"),
person("Sang Kyu", "Lee", role = c("aut"), email= "lsk0819@gmail.com"),
person("Jun", "Zhao", role = c("aut"), email = "zhaojun2021@hotmail.com"),
person("Hyoung-Moon", "Kim", role = c("aut", "ths"), email = "hmkim@konkuk.ac.kr"))
Yu-Kwang Kim [aut, cre, com],
Yu-Hyeong Jang [aut],
Jae Ho Chang [aut],
Sang Kyu Lee [aut],
Jun Zhao [aut],
Hyoung-Moon Kim [aut, ths]

Computation of the MLE for bivariate (interval) censored data
This package contains functions to compute the nonparametric
maximum likelihood estimator (MLE) for
the bivariate distribution of (X,Y), when
realizations of (X,Y) cannot be observed directly.
To be more precise, we consider the situation
where we observe a set of rectangles
(that we call 'observation rectangles') that are known
to contain the unobservable realizations of (X,Y). We
compute the MLE based on such a set of rectangles.
The methods can also be used for univariate censored data (see data set
'cosmesis'), and for
censored data with competing risks (see data set 'menopause').
We also provide functions to visualize
the observed data and the MLE.
Please
feel free to contact me with questions or suggestions about this package.
(This package contains the functionality
of the R-package 'bicreduc', which will no longer be maintained.)

This package contains functions to compute the nonparametric
maximum likelihood estimator (MLE) for
the bivariate distribution of (X,Y), when
realizations of (X,Y) cannot be observed directly.
To be more precise, we consider the situation
where we observe a set of rectangles
(that we call 'observation rectangles') that are known
to contain the unobservable realizations of (X,Y). We
compute the MLE based on such a set of rectangles.
The methods can also be used for univariate censored data (see data set
'cosmesis'), and for
censored data with competing risks (see data set 'menopause').
We also provide functions to visualize the observed data and the MLE.
(This package contains the functionality
of the R-package 'bicreduc', which will no longer be maintained.)