BsplineQuantRegGui

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Overview

‘BsplineQuantRegGui’ is an interactive Shiny interface for the ‘BsplineQuantReg’ package, providing a user-friendly way to perform quantile regression using B-splines with shape constraints.

The package is available on CRAN.

The underlying ‘BsplineQuantReg’ package provides the core regression methods for B-spline quantile regression with Karlin-Studden polynomial sign characterization, including degrees 1 to 4. From the version 2.3, the degrees 0 and greater than 5 are also available. It also provides a small set of robust functions in pure R for polynomial calculation, piecewise polynomial and B-spline manipulation, including differentiation, B-spline coefficients calculations and transformation to PP-form, evaluation of splines and pp polynomial as functions, and coefficients display in human readable form.

Features

This GUI application makes it easy to:

Load and visualize data (from multiple data sources: custom function, CSV files)
Interactive plotting: zoom, pan, and add knots by clicking on the plot
Configure B-spline parameters (degree 0 to 4 for constraints, and more than 5), visualise the basis.
Plot multiple curves with color management
Apply shape constraints (monotonicity, convexity, third derivative)
Manage constraints per region interactively
Run quantile regression with various solvers
Export reproducible R code
Run demo presets directly from the interface (according to degree)
Type of regression : quantile or mean-square (for ‘BsplineQuantReg’ version >=0.2.3)
Add/remove auto nknots, and manual knots with their multiplicities, see the corresponding basis with singularities.
control the application of constraints with derivatives.

Installation

From CRAN

install.packages("BsplineQuantRegGui")

From GitHub

install.packages("remotes")
remotes::install_github("alexandreabbes/BsplineQuantRegGui")

Quick Start

library(BsplineQuantRegGui)
# Launch the application
runGui()

The GUI will open in your default web browser.

Docker Deployment

A Docker image is available for easy deployment:

docker pull ghcr.io/alexandreabbes/bsplinequantreggui:latest

# Launch default version (advanced)
docker run -p 3838:3838 ghcr.io/alexandreabbes/bsplinequantreggui:latest

# Launch basic version
docker run -e APP_VERSION="basic" -p 3838:3838 ghcr.io/alexandreabbes/bsplinequantreggui:latest
#APP_VERSION :  either 1 || "basic"" or 2 || "advanced"

Then open the local adress “127.0.0.1:3838” in your browser (remove the quotes).

Requirements

Core Package

‘BsplineQuantReg’ >= 0.2.2 (Basic mode) or >= 0.2.6 (All features)

Version Compatibility

GUI Version	Core Version	Mode	Features
0.2.3	>= 0.2.2	Basic	Standard regression, constraints
0.2.3	0.2.6	Advanced	All features incl. multiplicity

Usage Guide

1. Data Selection

Click Test to generate sample data
Click Temp to load the global temperature dataset (1880-1992)
Click CSV to import your own data file
Adjust x range field, the number n of generated samples
Use the Custom function field to define your own data generator

2. Spline Configuration

Select Degree (1 to 4 is standard for constraints, but possible 0 to any)
Knots:

> - Adjust Auto knots count (placed as quantiles of knots)

> - Click Add knot to enter manual knot placement mode, then click on the plot. The two lists of knots auto and manual are almost independant and joined.

> - Click Clear knots to clear manual knots

> - Click on a knot to select it, click on “remove knot” to remove it.

3.Knots multiplicity

select or un-select “consider multiplicity”
click on a knot to select it
adjust multiplicity with + or -. Multiplicity of a knot is limited to degree+1

3b. View basis
go to the basis tab
select or un-select “consider multiplicity”
Chose a derivative level
Check the knots and their multiplicities
click “update basis” to view the Bspline Basis.

4. Constraints

Choose Uniform constraints to apply the same constraint everywhere
Choose Per region to define different constraints in specific regions
Click Select to enter region selection mode
Drag a rectangle on the plot to define the region
Adjust monotonicity, convexity, and third derivative constraints
Click Add region to apply the constraint
View/remove all active region constraints in the Regions tab

4. Run Analysis

Select the type of regression:quantile (\(L^1\) asymmetric absolute value) or mean (\(L^2\) quadratic).
Set the desired quantile.
Click Run to execute the regression with your chosen solver and type of regression (‘BsplineQuantReg’ >= ‘0.2.3’)
View results: the curve and the Information. Coefficients (on the B-spline Basis)
Equations of the spline in pp-form in a human readable form: (Des)Activate view.
Derivatives and check the constraints: (Des)Activate View.
View verbose output in the Console tab if verbose = TRUE is selected
Informations: Degree, tau, constraints, knots and coefficients
Data: Summary and table of the data

4b Reproduce analysis by your own

R Code tab: Generated code to reproduce the analysis

4c Check output in console

select “verbose” to print verbose output in the console tab.

5. Run Demos

Click on any demo to run and visualise it in the demo tab. The degree of the spline is that selected in the GUI.

Citation

citation(“BsplineQuantRegGui”)

If you use this package in your research, please cite:

@Article{Abbes2025, author = {Alexandre Abbes}, title = {Quantile Regression with Cubic Polynomial Splines under Shape Constraints with Applications}, year = {2025}, doi = {10.5281/zenodo.17427913} }

@Manual{Abbes2026R, author = {Alexandre Abbes}, title = {BsplineQuantReg: Quantile Regression with B-Splines of any degree, shape constraints for Degrees 0 to 4}, year = {2026}, note = {R package version 0.2.0}, url = {https://cran.r-project.org/package=BsplineQuantReg} }

License

GPL-3 (c) Alexandre Abbes (2026)

References

Abbes, A. (2025). Quantile Regression with Cubic Polynomial Splines under Shape Constraints with Applications. Zenodo. doi:10.5281/zenodo.17427913
Abbes, A. (2026). ‘BsplineQuantReg’: R Implementation of B-Spline Quantile Regression. https://cran.r-project.org/package=BsplineQuantReg
Abbes, A. (2026). ‘BsplineQuantRegpy’: Python Implementation of B-Spline Quantile Regression. https://pypi.org/project/BsplineQuantRegpy/