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- Visualisation tools to display output
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- Built-in theoretical models to test EWS
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`ewstools` makes use of [pandas](https://pandas.pydata.org/) for dataframe handling, [numpy](https://numpy.org/) for fast numerical computing, [plotly](https://plotly.com/graphing-libraries/) for visuliastion, [lmfit](https://lmfit.github.io/lmfit-py/) for least-squares minimisation, [arch](https://github.com/bashtage/arch) for bootstrapping methods, [statsmodels](https://www.statsmodels.org/stable/index.html) and [scipy](https://scipy.org/) for detrending methods, and [TensorFlow](https://www.tensorflow.org/install) for deep learning.
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`ewstools` makes use of [pandas](https://pandas.pydata.org/) for dataframe handling, [numpy](https://numpy.org/) for fast numerical computing, [plotly](https://plotly.com/graphing-libraries/) for visuliastion, [lmfit](https://lmfit.github.io/lmfit-py/) for least-squares minimisation, [arch](https://github.com/bashtage/arch) for bootstrapping methods, [EntropyHub](https://www.entropyhub.xyz/index.html) for entropy computations, [statsmodels](https://www.statsmodels.org/stable/index.html) and [scipy](https://scipy.org/) for detrending methods, and [TensorFlow](https://www.tensorflow.org/install) for deep learning.
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## Install

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