preProcess

Vector of independent and identically distributed numbers, not necessarily unique.

Parameter that governs the generation of weights: If <code>xgrid = NULL</code> a new sample
 of unique observations is generated with corresponding vector of weights. If <code>xgrid</code> is
 a positive number, observations are binned in a grid with grid length <code>xgrid</code>.
 Finally, an entire vector specifying a user-defined grid can be supplied.

xgrid

Generates weights from initial sample.

htest

nonparametric

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.

preProcess: Compute a weighted sample from initial observations

Description

Usage

Arguments

Value