_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
python-pca 2.10.2
Propagated dependencies: python-adjusttext@1.3.0 python-colourmap@1.2.1 python-datazets@1.1.4 python-matplotlib@3.10.8 python-numpy@2.3.1 python-pandas@2.3.3 python-scatterd@1.4.2 python-scikit-learn@1.7.2 python-scipy@1.16.3 python-statsmodels@0.14.5
Channel: guix-science
Location: guix-science/packages/statistics.scm (guix-science packages statistics)
Home page: https://erdogant.github.io/pca
Licenses: Expat
Build system: pyproject
Synopsis: Principal Component Analysis in Python
Description:

pca is a python package to perform PCA and to create insightful plots. The core of PCA is built on sklearn functionality to find maximum compatibility when combining with other packages. But this PCA package can do a lot more. Besides the regular Principal Components, it can also perform SparsePCA, TruncatedSVD, and provide you with the information that can be extracted from the components.

Total packages: 1