Estimate a Log-Concave Probability Density from Iid Observations
c(person(given = "Kaspar", family = "Rufibach", role = c("aut", "cre"), email = "kaspar.rufibach@gmail.com"),
    person(given = "Duembgen", family = "Lutz", role = "aut", email = "duembgen@stat.unibe.ch"))
Given independent and identically distributed observations X(1), ..., X(n), 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, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available.
Kaspar Rufibach [aut, cre],
  Duembgen Lutz [aut]