Given independent and identically distributed observations
X(1), ..., X(n), this package allows to compute the maximum
likelihood estimator (MLE) of a density as well as a smoothed
version of it under the assumption that the density is
log-concave, see Rufibach (2007) and Duembgen and Rufibach
(2009). The main function of the package is 'logConDens' that
allows computation of the log-concave MLE and its smoothed
version. In addition, we provide functions to compute (1) the
value of the density and distribution function estimates (MLE
and smoothed) at a given point (2) the characterizing functions
of the estimator, (3) to sample from the estimated
distribution, and (5) to compute a two-sample permutation test
based on log-concave densities. Finally, two datasets that have
been used to illustrate log-concave density estimation are made
available.

Given independent and identically distributed observations
X(1), ..., X(n), this package allows to compute the maximum
likelihood estimator (MLE) of a density as well as a smoothed
version of it under the assumption that the density is
log-concave, see Rufibach (2007) and Duembgen and Rufibach
(2009). The main function of the package is 'logConDens' that
allows computation of the log-concave MLE and its smoothed
version. In addition, we provide functions to compute (1) the
value of the density and distribution function estimates (MLE
and smoothed) at a given point (2) the characterizing functions
of the estimator, (3) to sample from the estimated
distribution, (5) to compute a two-sample permutation test
based on log-concave densities, (6) the ROC curve based on
log-concave estimates within cases and controls. Finally, three
datasets that have been used to illustrate log-concave density
estimation are made available.

Given independent and identically distributed observations X(1), ..., X(n), this package allows to
compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the
assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main
function of the package is 'logConDens' that allows computation of the log-concave MLE and its smoothed version.
In addition, we provide functions to compute (1) the value of the density and distribution function estimates
(MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from
the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities,
(6) the ROC curve based on log-concave estimates within cases and controls.
Finally, three datasets that have been used to illustrate log-concave density estimation are made available.