This package provides various forms of regression. The aim of these modules is to achieve clarity of implementation with a clear connection to the mathematical descriptions of the algorithms. The motivation for creating the package was the desire to learn about and explore the use of Principal Components Regression, Partial Least Squares regression and non-linear kernel-based Partial Least Squares regression.
Python 3.5 and Numpy 1.10 or greater are required as the new '@' matrix multiplication operator is used. If SciPy is available some linear algebra routines may be used as they can sometimes be faster than the routines in Numpy - however SciPy is not required. Matplotlib is used by the examples to display the results.
Full documentation of the API is maintained using Sphinx - see the doc
directory.
