Quantiles play a fundamental role in statistics. The quantile function defines the distribution of a random variable and, thus, provides a way to describe the data that is specular but equivalent to that given by the corresponding cumulative distribution function. There are many advantages in working with quantiles, starting from their properties. The renewed interest in their usage seen in the last years is due to the theoretical, methodological, and software contributions that have broadened their applicability. This paper presents the R package Qtools, a collection of utilities for unconditional and conditional quantiles.
quantreg, bayesQR, BSquare, lqmm, Qtools, boot, Rearrangement, mice
SocialSciences, Bayesian, Econometrics, Optimization, Robust, Survival, Environmetrics, Multivariate, OfficialStatistics, ReproducibleResearch, TimeSeries
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Geraci, "The R Journal: Qtools: A Collection of Models and Tools for Quantile Inference", The R Journal, 2016
BibTeX citation
@article{RJ-2016-037,
author = {Geraci, Marco},
title = {The R Journal: Qtools: A Collection of Models and Tools for Quantile Inference},
journal = {The R Journal},
year = {2016},
note = {https://doi.org/10.32614/RJ-2016-037},
doi = {10.32614/RJ-2016-037},
volume = {8},
issue = {2},
issn = {2073-4859},
pages = {117-138}
}