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 correlation coefficients RV, RV2 and SMI.
Unlike scikit-learn, which 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.
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.
numpy >= 1.11.3
Installation
Install hoggorm easily from the command line from the PyPI - the Python Packaging Index.
pip install hoggormDocumentation
Documentation at Read the Docs
Jupyter notebooks with examples of how to use Hoggorm together with the complementary plotting package hoggormplot.
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