Given a vector (φ_1, …, φ_m) representing the values of a piecewise linear concave function at x_1, …, x_m,etaphi returns a column vector with the entries
logconTwoSample
(Package: logcondens) :
Compute p-values for two-sample test based on log-concave CDF estimates
Compute p-values for a test for the null hypothesis of equal CDFs of two samples. The test statistic is reminiscient of Kolmogorv-Smirnov's, but instead of computing it for the empirical CDFs, this function computes it based on log-concave estimates for the CDFs.
confIntBootLogConROC_t0
(Package: logcondens) :
Function to compute a bootstrap confidence interval for the ROC curve at a given t, based on the log-concave ROC curve
This function computes a bootstrap confidence interval for the ROC curve at a given value false negative fraction (1 - specificity) t. The ROC curve estimate is based on log-concave densities, as discussed in Rufibach (2011).
logConCI
(Package: logcondens) :
Compute pointwise confidence interval for a density assuming log-concavity
Compute approximate confidence interval for the true log-concave density, on a grid of points. Two main approaches are implemented: In the first, the confidence interval at a fixed point is based on the pointwise asymptotic theory for the log-concave maximum likelihood estimator (MLE) developed in Balabdaoui, Rufibach, and Wellner (2009). In the second, the confidence interval is estimated via the boostrap.
providing 
.