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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


pip install nipals



To run the all tests run:


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


set PYTEST_ADDOPTS=--cov-append


PYTEST_ADDOPTS=--cov-append tox


0.5.5 (2022-09-28)

  • Added check for X-matrix with row full of NAs

0.5.4 (2021-05-07)

  • Fixed Packaging error (0.5.3 was never released)

0.5.3 (2021-05-06)

  • Fixed error on numpy version >= 1.19

  • Updated supported versions

  • Moved CI to Github Action (pt 1)

0.5.2 (2019-06-04)

  • Added compatibility with Nipals objects saved from pre-0.5 versions

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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