How to Install and Uninstall r-cran-logcondens Package on Ubuntu 21.10 (Impish Indri)

Last updated: November 22,2024

1. Install "r-cran-logcondens" package

This is a short guide on how to install r-cran-logcondens on Ubuntu 21.10 (Impish Indri)

$ sudo apt update $ sudo apt install r-cran-logcondens

2. Uninstall "r-cran-logcondens" package

Learn how to uninstall r-cran-logcondens on Ubuntu 21.10 (Impish Indri):

$ sudo apt remove r-cran-logcondens $ sudo apt autoclean && sudo apt autoremove

3. Information about the r-cran-logcondens package on Ubuntu 21.10 (Impish Indri)

Package: r-cran-logcondens
Architecture: all
Version: 2.1.5-3build1
Priority: optional
Section: universe/gnu-r
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 875
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-ks
Filename: pool/universe/r/r-cran-logcondens/r-cran-logcondens_2.1.5-3build1_all.deb
Size: 760584
MD5sum: 6602e09b26a03f77832631668bfde2cf
SHA1: b5e922f560d4fa23486943900720e4ccc054aefc
SHA256: f4d4c31e4ebce79136cb4716fbb6895265377c64719d13bda7084f4d32f923c3
SHA512: 1fb9354e433e072e2222b66cfa36514b621a86eb856d4452e328d6eec098e673e7598b838813e1abf0a9a0508e06da6be652236cd2f6a8c17bf1adfd8fd7ce63
Homepage: https://cran.r-project.org/package=logcondens
Description-en: 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.
Description-md5: be00f52d017f57cee12c9e0a134267b1