IKPLS

Spec sheet

Purpose

IKPLS is a Python package for fast partial least squares modeling, aimed at researchers and practitioners, particularly those working with large datasets.Read 12 Oct 2026 · ikpls.readthedocs.io

Audience

The project describes its intended users as researchers and practitioners doing PLS modeling.Read 12 Oct 2026 · ikpls.readthedocs.io

Deployment

desktopRead 23 Sep 2026 · ikpls.readthedocs.io

Free plan

YesRead 23 Sep 2026 · ikpls.readthedocs.io

Support

The contribution guidelines direct users with questions or bug reports to search or open a GitHub issue.Read 12 Oct 2026 · ikpls.readthedocs.io

License

PyPI lists the package under the Apache-2.0 license.Read 12 Oct 2026 · pypi.org

Algorithms

It implements Improved Kernel PLS Algorithms 1 and 2 for CPU, GPU, and TPU use.Read 12 Oct 2026 · ikpls.readthedocs.io

Compute

Its NumPy implementations run on CPUs, and its JAX implementations support CPUs, GPUs, and TPUs.Read 12 Oct 2026 · ikpls.readthedocs.io

Differentiability

The JAX implementations are end-to-end differentiable, allowing gradient propagation when PLS is used as a layer in a deep learning model.Read 12 Oct 2026 · ikpls.readthedocs.io

Documentation and examples

The project provides online documentation and examples covering fitting, prediction, and cross-validation with NumPy, JAX, and scikit-learn.Read 12 Oct 2026 · ikpls.readthedocs.io

Fast cross-validation

The package includes a fast cross-validation algorithm described as mathematically equivalent to classical cross-validation and quicker to run.Read 12 Oct 2026 · ikpls.readthedocs.io

Installation

The package installs with pip using `pip3 install ikpls`; JAX implementations require the optional `jax` extra.Read 12 Oct 2026 · ikpls.readthedocs.io

Installation requirement

PyPI lists the requirement as Python >=3.10 and <3.15.Read 12 Oct 2026 · pypi.org

JAX

Optional JAX implementations run on CPU, GPU, and TPU and support end-to-end differentiation.Read 12 Oct 2026 · ikpls.readthedocs.io

NumPy and scikit-learn

The NumPy CPU implementation includes a scikit-learn-conformant PLS wrapper that works with scikit-learn pipelines and cross-validation.Read 12 Oct 2026 · ikpls.readthedocs.io

Preprocessing

Fast cross-validation handles column-wise centering and scaling using training-set statistics to avoid validation-set data leakage, as well as sample-wise preprocessing such as convolution.Read 12 Oct 2026 · ikpls.readthedocs.io

Regression models

YesRead 23 Sep 2026 · ikpls.readthedocs.io

Sample weights

The NumPy and JAX implementations support sample-weighted PLS modeling.Read 12 Oct 2026 · ikpls.readthedocs.io

scikit-learn integration

The scikit-learn-conformant PLS wrapper can be used with scikit-learn pipelines and cross-validation tools.Read 12 Oct 2026 · ikpls.readthedocs.io

Scripting support

YesRead 23 Sep 2026 · ikpls.readthedocs.io

Transformations

NumPy and JAX implementations provide transform methods for dimensionality reduction to score space and inverse_transform methods for reconstruction.Read 12 Oct 2026 · ikpls.readthedocs.io

Weighted modeling

The package supports sample-weighted PLS modeling.Read 12 Oct 2026 · ikpls.readthedocs.io

Where Specifiction places it

• Statistical Analysis Software#88 of 1067.2