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Package for explorative multivariate statistics

Project description

hoggorm
=======

hoggorm is a Python package for explorative multivariate statistics in Python. It contains

* PCA (principal component analysis)
* PCR (principal component regression)
* PLSR (partial least squares regression)

- PLSR1 for single variable responses
- PLSR2 for multivariate responses
* matrix corrlation coefficients RV, RV2 and SMI.

Unlike `scikit-learn`_, whis is an excellent python machine learning package focusing on classification and predicition, hoggorm rather aims at understanding and interpretation of the variance in the data. hoggorm also also contains tools for prediction.

.. _scikit-learn: http://scikit-learn.org/stable/

Requirements
------------
Make sure that Python 3.5 or higher is installed. A convenient way to install Python and many useful packages for scientific computing is to use the `Anaconda distribution`_.

.. _Anaconda distribution: https://www.anaconda.com/download/

- numpy


Installation
------------

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

.. _PyPI - the Python Packaging Index: https://pypi.python.org/pypi

.. code-block:: bash

pip install hoggorm


Documentation
-------------

- Documentation at `Read the Docs`_
- Jupyter notebooks with `examples`_ of how to use Hoggorm together with the plotting package `hoggormplot`_.


.. _Read the Docs: http://hoggorm.readthedocs.io/en/latest
.. _examples: https://github.com/khliland/hoggormExamples
.. _hoggormlot: https://github.com/olivertomic/hoggormPlot


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