software.html

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    These are some repos in which I have made relevant contributions. They are all used in my publications can be downloaded from my <a href = 'https://github.com/javiergonzalezh'> GitHub account</a>. Most of it is written in R, Matlab and Python. Please contact me if you have any question or comment.
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    <h1>Emukit</h1>
<a href="https://amzn.github.io/emukit/">Emukit</a> is a Python-based toolbox of various methods in uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
    It is still in a pre-release phase. Together with, you can find the <a href ="https://amzn.github.io/emukit-playground/#!/"> Emukit-playground</a>,
    an interactive demo developed by Adam Hisrt useful to illustrate different concepts in emulation and uncertainty quantification.
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    <h1>GPyOpt</h1>
    <a href="https://sheffieldml.github.io/GPyOpt/">GPyOpt</a> is a library for Bayesian Optimization and experimental design. It is written in python.
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    <h1>dpp</h1>
    <a href="https://github.com/javiergonzalezh/dpp">dpp</a> is a small Python package to sample from determinantal point processes. It is based on the Matlab code of<a href="http://web.eecs.umich.edu/~kulesza/"> Alex Kulesza</a> and contains Python wrappers that make these methods usable in Python.
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    <h1>Odest</h1>
<a href="./docs/odest-manual.pdf">odest</a> is a R-package estimating parameters of systems of Ordinary Differential equations. The method is based on a regularization approach in Reproducing kernel Hilbert Spaces.

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