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A module for calculation of PCA with the NIPALS algorithm

Project description

A module for calculation of PCA and PLS with the NIPALS algorithm. Based on the R packages nipals and pcaMethods as well as the statistical appendixes to “Introduction to Multi- and Megavariate Data Analysis using Projection Methods (PCA & PLS)” by Eriksson et. al. Tested to give same results as the above packages and Simca, with some rounding errors.

  • Free software: MIT license

Installation

pip install nipals

Development

To run the all tests run:

tox

Note, to combine the coverage data from all the tox environments run:

Windows
set PYTEST_ADDOPTS=--cov-append
tox
Other
PYTEST_ADDOPTS=--cov-append tox

Changelog

0.5.1 (2019-05-23)

  • Added checks for, and optional removal of, zero variance in variables
  • Added support for Python 3.7
  • (0.5.0 was never released due to failing CI tests)

0.4.3 (2018-04-24)

  • Fixed test that failed after last bug fix

0.4.2 (2018-04-24)

  • Fixed bug with selection of starting column for cross validation of PCA

0.4.1 (2018-04-09)

  • Fixed bug with cross validation of PCA

0.4.0 (2018-04-09)

  • Added cross validations
  • Added calculation of distance to model with plots
  • Added model overview plots

0.3.0 (2018-04-05)

  • Added R2X and R2Y to the PLS class
  • Made plot color selectable also for scoreplots without classes

0.2.0 (2018-03-29)

  • Added a PLS class
  • Improved plotting
  • Fixed some problems with missing/infinite values

0.1.0 (2018-03-14)

  • First release on PyPI.

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