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.)

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 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.

Computation of the MLE for Bivariate Interval Censored Data
person("Marloes", "Maathuis", role= c("aut", "cre"),
email= "maathuis@stat.math.ethz.ch")
We provide 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 in R^2 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.