ChainsAddiction
ChainsAddiction is an easy to use tool for time series analysis using
discrete-time Hidden Markov Models. It is written in C as a numpy-based
Python extension module.
This is the fork maintained by Rolf Bader (canonical repository on Codeberg). It is part of the apollon / chainsaddiction / comsar stack; see the comsar repository for the full manual.
Installation
Install from PyPI (recommended, no compiler needed)
Pre-compiled wheels are provided for Windows, macOS and Linux (CPython 3.9–3.13):
python3 -m pip install bader-chainsaddiction
The import name is unchanged — you still write import chainsaddiction.
Install from source
Building from source requires
- Python >= 3.9
- pip, setuptools, NumPy
- a C compiler (MSVC Build Tools on Windows, Xcode Command Line Tools on macOS, gcc/clang on Linux)
Then:
git clone https://codeberg.org/rbader/chainsaddiction path/to/ca
cd path/to/ca
python3 -m pip install .
Notes
Currently only Poisson-distributed HMM are implemented.
1 maintainer
Metadata
1 maintainer
Release files for bader-chainsaddiction 0.2.5
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
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| bader_chainsaddiction-0.2.5.tar.gz | 17.7 kB | With provenance | Details |
Built distributions (wheels)
Total release size: 1.5 MB
Release files / bader_chainsaddiction-0.2.5.tar.gz
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