scientisttools is a Python
package dedicated to multivariate Exploratory Data Analysis.
- It performs classical principal component methods :
- Principal Components Analysis (PCA)
- Principal Components Analysis with partial correlation matrix (PPCA)
- Weighted Principal Components Analysis (WPCA)
- Expectation-Maximization Principal Components Analysis (EMPCA)
- Exploratory Factor Analysis (EFA)
- Classical Multidimensional Scaling (CMSCALE)
- Metric and Non - Metric Multidimensional Scaling (MDS)
- Correspondence Analysis (CA)
- Multiple Correspondence Analysis (MCA)
- Factor Analysis of Mixed Data (FAMD)
- In some methods, it allowed to add supplementary informations such as supplementary individuals and/or variables.
- It provides a geometrical point of view, a lot of graphical outputs.
- It provides efficient implementations, using a scikit-learn API.
scientisttools requires
Python 3
Numpy >= 1.24.3
Matplotlib >= 3.5.3
Scikit-learn >= 1.2.2
Pandas >= 2.0.0
Plotnine >= 0.10.1
Plydata >= 0.4.3
You can install scientisttools using pip
:
pip install scientisttools
Tutorial are available
https://github.com/enfantbenidedieu/scientisttools/blob/master/ca_example2.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/classic_mds.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/efa_example.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/famd_example.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/ggcorrplot.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/mca_example.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/mds_example.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/partial_pca.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/pca_example.ipynb
Duvérier DJIFACK ZEBAZE (duverierdjifack@gmail.com)