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# openMCMC
openMCMC is a package for constructing Bayesian models from distributional components, and then doing parameter
estimation using Markov Chain Monte Carlo (MCMC) methods. The package supports a number of standard distributions used
in Bayesian modelling (e.g. Normal, gamma, uniform), and a number of simple functional forms for the parameters of
these distributions. For a model constructed in the toolbox, a number of different MCMC algorithms are available,
including simple random walk Metropolis-Hastings, manifold MALA, exact samplers for conjugate distribution choices,
and reversible-jump MCMC for parameters with an unknown dimensionality.
***
# Installing openMCMC as a package
Suppose you want to use this openMCMC package in a different project.
You can install it from [PyPi](https://pypi.org/project/openmcmc/) through pip
`pip install openmcmc`.
Or you could clone the repository and install it from the source code. After activating the environment you want to
install openMCMC in, open a terminal, move to the main openMCMC folder
where pyproject.toml is located and run `pip install .`, optionally you can pass the `-e` flag is for editable mode.
All the main options, info and settings for the package are found in the pyproject.toml file which sits in this repo
as well.
***
# Examples
For some examples on how to use this package please check out these [Examples](https://github.com/sede-open/openMCMC/blob/main/examples)
***
# Contribution
This project welcomes contributions and suggestions. If you have a suggestion that would make this better you can
simply open an issue with a relevant title. Don't forget to give the project a star! Thanks again!
For more details on contributing to this repository, see the [Contributing guide](https://github.com/sede-open/openMCMC/blob/main/CONTRIBUTING.md).
***
# Licensing
Distributed under the Apache License Version 2.0. See the [license file](https://github.com/sede-open/openMCMC/blob/main/LICENSE.md) for more information.
Raw data
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"description": "<!--\nSPDX-FileCopyrightText: 2024 Shell Global Solutions International B.V. All Rights Reserved.\n\nSPDX-License-Identifier: Apache-2.0\n-->\n\n<div align=\"center\">\n\n[](https://pypi.org/project/openmcmc/)\n[](https://pypi.org/project/openmcmc/)\n[](https://opensource.org/licenses/Apache-2.0)\n[](https://github.com/psf/black)\n[](https://github.com/sede-open/openMCMC/actions/workflows/main.yml)\n\n[](https://sonarcloud.io/summary/new_code?id=pyelq_openmcmc)\n[](https://sonarcloud.io/summary/new_code?id=pyelq_openmcmc)\n[](https://sonarcloud.io/summary/new_code?id=pyelq_openmcmc)\n[](https://sonarcloud.io/summary/new_code?id=pyelq_openmcmc)\n[](https://sonarcloud.io/summary/new_code?id=pyelq_openmcmc)\n[](https://sonarcloud.io/summary/new_code?id=pyelq_openmcmc)\n[](https://sonarcloud.io/summary/new_code?id=pyelq_openmcmc)\n[](https://sonarcloud.io/summary/new_code?id=pyelq_openmcmc)\n</div>\n\n# openMCMC\nopenMCMC is a package for constructing Bayesian models from distributional components, and then doing parameter \nestimation using Markov Chain Monte Carlo (MCMC) methods. The package supports a number of standard distributions used \nin Bayesian modelling (e.g. Normal, gamma, uniform), and a number of simple functional forms for the parameters of \nthese distributions. For a model constructed in the toolbox, a number of different MCMC algorithms are available, \nincluding simple random walk Metropolis-Hastings, manifold MALA, exact samplers for conjugate distribution choices, \nand reversible-jump MCMC for parameters with an unknown dimensionality.\n***\n\n# Installing openMCMC as a package\nSuppose you want to use this openMCMC package in a different project.\nYou can install it from [PyPi](https://pypi.org/project/openmcmc/) through pip \n`pip install openmcmc`.\nOr you could clone the repository and install it from the source code. After activating the environment you want to \ninstall openMCMC in, open a terminal, move to the main openMCMC folder\nwhere pyproject.toml is located and run `pip install .`, optionally you can pass the `-e` flag is for editable mode.\nAll the main options, info and settings for the package are found in the pyproject.toml file which sits in this repo\nas well.\n\n***\n\n# Examples\nFor some examples on how to use this package please check out these [Examples](https://github.com/sede-open/openMCMC/blob/main/examples)\n\n***\n# Contribution\nThis project welcomes contributions and suggestions. If you have a suggestion that would make this better you can \nsimply open an issue with a relevant title. Don't forget to give the project a star! Thanks again!\n\nFor more details on contributing to this repository, see the [Contributing guide](https://github.com/sede-open/openMCMC/blob/main/CONTRIBUTING.md).\n\n***\n# Licensing\n\nDistributed under the Apache License Version 2.0. See the [license file](https://github.com/sede-open/openMCMC/blob/main/LICENSE.md) for more information.\n\n",
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