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r-cran-logcondens package

r-cran-logcondens 2.1.8-1 source package in Ubuntu

Changelog

r-cran-logcondens (2.1.8-1) unstable; urgency=medium

  * New upstream version

 -- Andreas Tille <email address hidden>  Thu, 31 Aug 2023 17:07:15 +0200

Upload details

Uploaded by:
Debian R Packages Maintainers
Uploaded to:
Sid
Original maintainer:
Debian R Packages Maintainers
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Oracular release universe misc
Noble release universe misc

Builds

Noble: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
r-cran-logcondens_2.1.8-1.dsc 2.1 KiB 880e56e7b65a71681e7148034d768f3a6ca6d674ea91e956a2b5c8dcc23e4735
r-cran-logcondens_2.1.8.orig.tar.gz 562.4 KiB f139206e47d1077ffcb39248450c1d7ce2ac892cb9264dd0e1ace92532162a00
r-cran-logcondens_2.1.8-1.debian.tar.xz 2.7 KiB d85c9beeb79052ebc93ca68738b4f0e0b79939dc5b73f1d1ef30a34a90edb6e2

Available diffs

No changes file available.

Binary packages built by this source

r-cran-logcondens: GNU R estimate a log-concave probability density from Iid observations

 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, the package
 provides 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.