Skip to main content

Package for explorative multivariate statistics

Project description

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

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 hoggorm

Documentation

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for hoggorm, version 0.13.3
Filename, size File type Python version Upload date Hashes
Filename, size hoggorm-0.13.3-py2.py3-none-any.whl (47.6 kB) File type Wheel Python version py2.py3 Upload date Hashes View hashes
Filename, size hoggorm-0.13.3.tar.gz (44.3 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page