Ubuntu
r-cran-logcondens package

r-cran-logcondens 2.1.7-1 source package in Ubuntu

Changelog

r-cran-logcondens (2.1.7-1) unstable; urgency=medium

  * Disable reprotest
  * New upstream version
  * Standards-Version: 4.6.2 (routine-update)
  * Reorder sequence of d/control fields by cme (routine-update)

 -- Andreas Tille <email address hidden>  Fri, 13 Jan 2023 09:11:45 +0100

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
Mantic release universe misc
Lunar release universe misc

Builds

Lunar: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
r-cran-logcondens_2.1.7-1.dsc 2.1 KiB 1ce48ddb9793828034661242fc56f034703d36c1c20b1ccd38101b550acd7600
r-cran-logcondens_2.1.7.orig.tar.gz 562.7 KiB 1ad887571afe2d3f66676c59b32ed8fb2fa13fada14f0a1834fbbcfe983acbf8
r-cran-logcondens_2.1.7-1.debian.tar.xz 2.7 KiB 106f0e51466ea9f12d09c677a283aa7694beaf095b3f09469742a25abf48800b

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.