We also provide functions to visualize the observed data and the MLE.
Packaged: Thu Aug  2 09:32:08 2007; marloes

Packaged: Thu Aug  2 17:31:11 2007; marloes

Package: MLEcens
Version: 0.1-3
Date: 2010-02-15
Title: Computation of the MLE for bivariate (interval) censored data
Author: Marloes Maathuis <maathuis@stat.math.ethz.ch>
Maintainer: Marloes Maathuis <maathuis@stat.math.ethz.ch>
Depends:
Description: 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.)
License: GPL (>= 2)
URL: http://stat.ethz.ch/~maathuis/
Packaged: 2010-04-15 12:06:35 UTC; maathuis
Repository: CRAN
Date/Publication: 2010-04-15 13:49:21

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

Title: Computation of the MLE for Bivariate Interval Censored Data
Authors@R: person("Marloes", "Maathuis", role= c("aut", "cre"),
Description: We provide functions to compute the nonparametric
where we observe a set of rectangles in R^2 that are known

Title: Machine Learning Forest Simulator
Description: Climate-sensitive forest simulator based on the principles of
machine learning. It stimulates all key processes in the forest: radial growth,
height growth, mortality, crown recession, regeneration and harvesting. The
method for predicting tree heights was described by Skudnik and Jevšenak
(2022) <doi:10.1016/j.foreco.2022.120017>, while the method for predicting
basal area increments (BAI) was described by Jevšenak and Skudnik (2021)