nipals 0.5.4

A module for calculation of PCA with the NIPALS algorithm

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

Documentation

https://python-nipals.readthedocs.io/

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

0.5.4 (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.