Science Score: 23.0%
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○Scientific vocabulary similarity
Low similarity (16.2%) to scientific vocabulary
Repository
watson package
Basic Info
- Host: GitHub
- Owner: lsablica
- License: gpl-3.0
- Language: C++
- Default Branch: main
- Size: 35.2 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
# watson
[](https://www.r-project.org/)
[](https://isocpp.org/)
[](http://arma.sourceforge.net/)
[](https://cran.r-project.org/package=watson)
[](https://opensource.org/licenses/GPL-3.0)
[](https://github.com/lsablica/watson/actions/workflows/rhub.yaml)
*A high-performance computational framework for the Watson distribution and its mixtures.*
[Key Features](#key-features) •
[Project Overview](#project-overview) •
[Installation](#installation) •
[Citation](#citation)
Key Features
🚀 State-of-the-Art Statistical Modeling - Designed for axial data on high-dimensional spheres. - Fit mixtures of Watson distributions with ease using an optimized Expectation-Maximization (EM) algorithm.
🧠 Built on Rigorous Mathematics - Powered by theoretical work on bounds for Kummer’s function and its derivatives: - Sablica & Hornik (2022): "On bounds for Kummer’s function ratio". - Sablica & Hornik (2024): "Family of integrable bounds for the logarithmic derivative of Kummer’s function".
⚡ Speed - Heavy lifting implemented in C++ using Armadillo, delivering unmatched computational performance. - Efficient handling of sparse matrices and large-scale data.
📊 Advanced Statistical Features - Supports custom initialization and dynamic elimination of small clusters. - Automated selection of optimal rejection sampling algorithms based on parameters.
📖 Comprehensive Documentation - Full documentation and examples are available, with the package paper under review in the Journal of Statistical Computing.
Project Overview
watson is the go-to R package for modeling and analyzing axial data using the Watson distribution. It provides researchers, data scientists, and statisticians with the tools needed to:
- Simulate data from Watson distributions and their mixtures.
- Fit complex models to high-dimensional axial data.
- Accurately estimate parameters using robust numerical methods.
Why Axial Data?
Axial data are unit vectors on a sphere where directions are indistinguishable (e.g., $x$ and $-x$ are equivalent). These data arise naturally in fields such as:
- Structural Geology: Modeling rock magnetism or fault planes.
- Biostatistics: Analyzing molecular orientations.
- Machine Learning: Embedding spaces and sentiment analysis.
With watson, you can unlock the full potential of axial data, leveraging a framework that combines theoretical results with computational power.
Installation
The package is available on CRAN:
R
install.packages("watson")
Citation
If you use watson in your research, please cite:
bibtex
@article{watson2025,
title = "{watson: An {R} Package for Fitting Mixtures of {Watson} Distributions}",
author = {Lukas Sablica and Kurt Hornik and Josef Leydold},
journal = {Journal of Statistical Software},
year = 2025,
note = {Accepted for publication}
}
Owner
- Login: lsablica
- Kind: user
- Repositories: 1
- Profile: https://github.com/lsablica
GitHub Events
Total
- Push event: 15
Last Year
- Push event: 15
Packages
- Total packages: 1
-
Total downloads:
- cran 269 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 7
- Total maintainers: 1
cran.r-project.org: watson
Fitting and Simulating Mixtures of Watson Distributions
- Homepage: https://github.com/lsablica/watson
- Documentation: http://cran.r-project.org/web/packages/watson/watson.pdf
- License: GPL-3
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Latest release: 0.0.1
published over 3 years ago