Hidden Markov Models for Julia.
HMMBase provides a lightweight and efficient abstraction for hidden Markov models in Julia. Most HMMs libraries only support discrete (e.g. categorical) or normal distributions. In contrast HMMBase builds upon Distributions.jl to support arbitrary univariate and multivariate distributions.
The goal is to provide well-tested and fast implementations of the basic HMMs algorithms such as the forward-backward algorithm, the Viterbi algorithm, and the MLE estimator. More advanced models, such as Bayesian HMMs, can be built upon HMMBase.
See HMMBase.jl - A lightweight and efficient Hidden Markov Model abstraction for more details on the motivation behind this package.
The package can be installed with the Julia package manager.
From the Julia REPL, type
] to enter the Pkg REPL mode and run:
pkg> add HMMBase
The package is tested against Julia 1.0 and the nightly builds of the Julia
master branch on Linux and macOS.
Contributions are very welcome, as are feature requests and suggestions. Please open an issue if you encounter any problems.
9 days ago