‘probaverse’ makes probability distributions routine data structures in R. Inspired by the cohesive design of the ‘tidyverse’, it installs and attaches the core packages in the ‘probaverse’ suite with a single library() call.
Target Audience
The ‘probaverse’ is designed for anyone who wants to work with probability distributions in R. It is particularly useful for data scientists, statisticians, and researchers who require the flexibility to develop advanced statistical models. Special focus is put on risk and hazard modelling, where distributions are heavily used in practice; therefore, those in fields such as finance, insurance, environmental science, and engineering may find the ‘probaverse’ particularly appealing.
Core Features
Advanced statistical models can rarely be achieved with out-of-the-box distributions typically found in probability textbooks. Instead, distributions need to be manipulated, combined, and refined to match specific benchmarks like data or expert judgement. They need to be evaluated in ways that are meaningful to your application. The ‘probaverse’ packages work in harmony to provide a workbench for this process using a human-friendly interface.
This workbench would be too vast and unweidly implemented as a single package. Instead, each concern is separated into separate packages.
- ‘distionary’ provides tools to defines and evaluate distributions by specifying properties that you know about the distribution.
- ‘distplyr’ allows you to manipulate and combine distributions so that they can more realistically represent your system.
- ‘famish’ aims to ground distributions based on benchmarks, such as estimating distributions from data, or refining distributions to match expert judgement.
Usage
Load every ‘probaverse’ package with a single command:
library(probaverse)
#> ── Attaching core probaverse packages ──────────────────────────────────────────
#> ✔ distionary 0.1.0 Create and Evaluate Probability Distributions
#> ✔ distplyr 0.2.0 Manipulate and Combine Probability Distributions
#> ✔ famish 0.2.0 Flexibly Tune Families of Probability DistributionsFuture Goals
The ‘probaverse’ has bigger long-term goals than what it’s currently capable of. Here are just a few of the longer term goals:
- Implementing multivariate distributions, especially through copulas.
- Extending options for fitting non-parametric distributions.
- Creating custom distribution families rather than just individual distributions.
- Improving support for distributions with discrete components.
Additional features will be added as development continues. We appreciate your patience and welcome contributions! Please see the contributing guide to get started.
Acknowledgments
The creation of ‘probaverse’ would not have been possible without the support of BGC Engineering Inc., the R Consortium (via ‘distionary’), the European Space Agency, and the Politecnico di Milano, The University of British Columbia, the Natural Science and Engineering Research Council of Canada (NSERC). The authors would also like to thank the reviewers from ROpenSci for their insightful feedback in the core ‘distionary’ package that powers the ‘probaverse’ suite. Lastly, thanks to the developers of ‘tidyverse’ for providing a model for cohesive design and appropriate structure of a meta-package.
Code of Conduct
Please note that the ‘probaverse’ project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.