Open Source Software Overview

Over the past few years, I have had the opportunity to develop and contribute to a range of open-source software packages, primarily in R and Python. While the following list is not exhaustive, it highlights the most recent and significant packages—all of which are publicly available for the community to use.

colorspace (web, R, Python)

Color is vital for visualizing and communicating scientific information, but selecting colors that are accessible to all viewers can be tricky. While many graphics tools offer predefined palettes, customizing them using perceptual principles is often difficult. The colorspace package, in both R and Python, enables users to easily design custom palettes, evaluate them, and check for color vision deficiencies. It also supports transformations between color spaces and offers tools for adjusting colors and calculating contrast ratios.

The HCLwizard: Interactive website

Interested to try it out? HCLwizard.org grants access to the interactive user interfaces of the R package.

• HCLwizard: https://hclwizard.org

R colorspace

• Package (CRAN): https://cran.r-project.org/package=colorspace
• Documentation: https://colorspace.r-forge.r-project.org/
• Repository: http://r-forge.r-project.org/projects/colorspace
• Article: Zeileis A, Fisher JC, Hornik K, Ihaka R, McWhite CD, Murrell P, Stauffer R, Wilke CO (2020). “colorspace: A Toolbox for Manipulating and Assessing Colors and Palettes.” Journal of Statistical Software, 96(1), 1–49. doi:10.18637/jss.v096.i01.

Python colorspace

• Package (PyPI): https://pypi.org/project/colorspace
• Documentation: https://retostauffer.github.io/python-colorspace/
• Repository: https://github.com/retostauffer/python-colorspace

R exams

The R package exams offers a comprehensive solution for automatic exam generation, supporting mass production of randomized written exams (PDF), imports for various learning management systems, web-based formats for self-learning, and more.

• Package (CRAN): https://cran.r-project.org/package=exams
• Documentation: https://r-exams.org/
• Repository: https://r-forge.r-project.org/projects/exams
• Article: Grün B, Zeileis A (2009). “Automatic Generation of Exams in R.” Journal of Statistical Software, 29(10), 1–14. doi:10.18637/jss.v029.i10.

The R package exams is complemented by exams2forms, which allows embedding dynamic R/exams exercises as interactive forms in HTML documents, from standalone HTML files to online books and websites.

• Package (CRAN): https://cran.r-project.org/package=exams2forms
• Tutorial: https://www.r-exams.org/tutorials/exams2forms/

R crch

A package for estimating truncated or censored regression models with potential heteroscedasticity.

• Package (CRAN): https://cran.r-project.org/package=crch
• Documentation: https://topmodels.r-forge.r-project.org/crch/
• Repository: https://r-forge.r-project.org/projects/topmodels
• Article: Messner JW, Mayr GJ, Zeileis A (2016). “Heteroscedastic Censored and Truncated Regression with crch.” The R Journal, 8(1), 173–181. doi:10.32614/RJ-2016-012.

R topmodels

Unified infrastructure for probabilistic models and distributional regressions: Probabilistic forecasting of in-sample and out-of-sample of probabilities, densities, quantiles, and moments. Probabilistic residuals and scoring via log-score (or log-likelihood), (continuous) ranked probability score, etc. Diagnostic graphics like rootograms, PIT histograms, (randomized) quantile residual Q-Q plots, and reliagrams (reliability diagrams).

• Documentation: https://topmodels.r-forge.r-project.org/
• Repository: https://r-forge.r-project.org/projects/topmodels

R annex

The R package annex offers a user-friendly interface for processing air quality data, specifically for contributors to the IEA EBC Annex 86 project on energy-efficient indoor air quality management in residential buildings. It streamlines raw data processing and facilitates data anonymization.

• Documentation: https://iea-ebc-annex86.github.io/annex/
• Repository: https://github.com/IEA-EBC-Annex86/annex/

foehnix (R, Python)

The foehnix package provides a toolbox for automated probabilistic foehn wind classification based on two-component mixture models (foehn mixture models); an unsupervised statistical model to identify unobserveable clusters or components in a data set.

R foehnix

• Documentation: https://retostauffer.github.io/Rfoehnix
• Repository: https://github.com/retostauffer/Rfoehnix/

Python foehnix

• Documentation: https://matthiasdusch.github.io/foehnix-python
• Repository: https://github.com/matthiasdusch/foehnix-python

R gsdata

Since 2023, Austrias national weather service GeoSphere (formerly known as ZAMG) provides a wide range of meteorological data to the public via their Geosphere Data Hub.

The R package gsdata provides easy access a series of products and will be extended in the future to grant access to a larger variety of data, including spatial data (i.e., spatial data).

• Documentation: https://retostauffer.github.io/gsdata/
• Repository: https://github.com/retostauffer/gsdata
• Stationdata examples: https://retostauffer.github.io/gsdata/articles/stationdata.html