A collection of parametric quantile regression models for bounded data. At present, the package provides 13 parametric quantile regression models. It can specify regression structure for any quantile and shape parameters. It also provides several S3 methods to extract information from fitted model, such as residual analysis, prediction, plotting, and model comparison. For more computation efficient the [dpqr]'s, likelihood, score and hessian functions are written in C++. For further details see Mazucheli et. al (2022)
unitquantreg R package 
The goal of unitquantreg is to provide tools for estimation and
inference on parametric quantile regression models for bounded data.
We developed routines with similar interface as stats::glm function,
which contains estimation, inference, residual analysis, prediction, and
model comparison.
For more computation efficient the [dpqr]’s, likelihood, score and
hessian functions are vectorized and written in C++.
You can install the stable version from CRAN with:
install.packages("unitquantreg")
Or you can install the development version from GitHub with:
if(!require(remotes)) install.packages('remotes')
remotes::install_github("AndrMenezes/unitquantreg", build_vignettes = TRUE)
You can then load the package
library(unitquantreg)
and look at user manuals typing:
vignette("unitquantreg")
vignette("structure_functionality")
citation("unitquantreg")
#>
#> To cite unitquantreg in publications use:
#>
#> Menezes A, Mazucheli J (2021). _unitquantreg: Parametric quantile
#> regression models for bounded data_. R package version 0.0.3,
#> <https://andrmenezes.github.io/unitquantreg/>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {unitquantreg: {P}arametric quantile regression models for bounded data},
#> author = {Andr{'}e F. B. Menezes and Josmar Mazucheli},
#> note = {R package version 0.0.3},
#> url = {https://andrmenezes.github.io/unitquantreg/},
#> year = {2021},
#> }
The unitquantreg package is released under the Apache License, Version
2.0. Please, see file
LICENSE.md.