PLSLib is a collection of Python and R scripts demonstrating the different algorithms detailed in Partial Least Squares Regression and Related Dimension Reduction Methods by R. Dennis Cook and Liliana Forzani, available here.
We are open to pull requests for translating R scripts into Python and also bugfixes, if you find any.
To download the files, either download the ZIP file by clicking on the green <> Code button:
Or use git to clone the repository to your local:
- cd into the directory where you want to put the scripts
- run
git clone https://github.com/MarcoTab/PLSLib.git - the scripts to reproduce tables and figures are found in the directory
examples.
- Python must be version 3.8 or newer.
- Install all required packages using the provided file in the top level directory:
pip3 install -r requirements.txt. - Run scripts from the top level directory (i.e. the directory where this README is located). Example:
user@machine: ~/path/to/PLSLib$ python3 example/chapter4/mussels.py
- R must be version 4.2.2 or newer
- Set your working directory to the top level directory in the repository before running scripts. I.e.
setwd("~/path/to/PLSLib").