lqr: Robust Linear Quantile Regression

It fits a robust linear quantile regression model using a new family of zero-quantile distributions for the error term. Missing values and censored observations can be handled as well. This family of distribution includes skewed versions of the Normal, Student's t, Laplace, Slash and Contaminated Normal distribution. It also performs logistic quantile regression for bounded responses as shown in Galarza et.al.(2020) <doi:10.1007/s13571-020-00231-0>. It provides estimates and full inference. It also provides envelopes plots for assessing the fit and confidences bands when several quantiles are provided simultaneously.

Version: 5.2
Imports: graphics, stats, spatstat.univar, numDeriv, MomTrunc, quantreg, MASS
Suggests: ald
Published: 2024-07-12
DOI: 10.32614/CRAN.package.lqr
Author: Christian E. Galarza, Luis Benites, Marcelo Bourguignon, Victor H. Lachos
Maintainer: Christian E. Galarza <cgalarza88 at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
In views: MissingData
CRAN checks: lqr results


Reference manual: lqr.pdf


Package source: lqr_5.2.tar.gz
Windows binaries: r-devel: lqr_5.2.zip, r-release: lqr_5.2.zip, r-oldrel: lqr_5.2.zip
macOS binaries: r-release (arm64): lqr_5.2.tgz, r-oldrel (arm64): lqr_5.2.tgz, r-release (x86_64): lqr_5.2.tgz, r-oldrel (x86_64): lqr_5.2.tgz
Old sources: lqr archive

Reverse dependencies:

Reverse imports: qrLMM, qrNLMM


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