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hoggorm

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hoggorm is a Python package for explorative multivariate statistics in Python. It contains the following methods:

  • PCA (principal component analysis)
  • PCR (principal component regression)
  • PLSR (partial least squares regression)
    • PLSR1 for single variable responses
    • PLSR2 for multivariate responses
  • matrix correlation coefficients RV, RV2 and SMI.

Unlike scikit-learn, which is an excellent python machine learning package focusing on classification, regression, clustering and predicition, hoggorm rather aims at understanding and interpretation of the variance in the data. hoggorm also contains tools for prediction. The complementary package hoggormplot can be used for visualization of results of models trained with hoggorm.

Examples

Below are links to some Jupyter notebooks that illustrate how to use hoggorm and hoggormplot with the methods mentioned above. All examples are also found in the examples folder.

Requirements

Make sure that Python 3.6 or higher is installed. A convenient way to install Python and many useful packages for scientific computing is to use the Anaconda distribution.

  • numpy >= 1.9

Installation

Using pip

PyPI Downloads PyPI Downloads PyPI Downloads

Install hoggorm easily from the command line from the PyPI - the Python Packaging Index.

pip install hoggorm

Using conda

Conda Downloads Conda Version

You can install using the conda package manager by running

conda install -c conda-forge hoggorm

Documentation

https://readthedocs.org/projects/hoggorm/badge/?version=latest

Citing hoggorm

If you use hoggorm in a report or scientific publication, we would appreciate citations to the following paper:

Tomic et al., (2019). hoggorm: a python library for explorative multivariate statistics. Journal of Open Source Software, 4(39), 980, https://doi.org/10.21105/joss.00980

Bibtex entry:

@article{hoggorm,
  title={hoggorm: a python library for explorative multivariate statistics},
  author={Tomic, Oliver and Graff, Thomas and Liland, Kristian Hovde and N{\ae}s, Tormod},
  journal={The Journal of Open Source Software},
  volume={4},
  number={39},
  year={2019},
  doi={10.21105/joss.00980},
  url={http://joss.theoj.org/papers/10.21105/joss.00980}
}