From a28478281b36f64aec90e444cd2d9f2ce7b82559 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ole=20Engstr=C3=B8m?= Date: Mon, 5 Aug 2024 00:18:09 +0200 Subject: [PATCH] Updated README --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 2843405..4fbd915 100644 --- a/README.md +++ b/README.md @@ -18,6 +18,9 @@ The `ikpls` software package provides fast and efficient tools for PLS (Partial Least Squares) modeling. This package is designed to help researchers and practitioners handle PLS modeling faster than previously possible - particularly on large datasets. +## Citation +If you use the `ikpls` software package for your work, please cite [this Journal of Open Source Software paper](https://joss.theoj.org/papers/10.21105/joss.06533). If you use the fast cross-validation algorithm implemented in `ikpls.fast_cross_validation.numpy_ikpls`, please also cite [this arXiv preprint](https://arxiv.org/abs/2401.13185). + ## Unlock the Power of Fast and Stable Partial Least Squares Modeling with IKPLS Dive into cutting-edge Python implementations of the IKPLS (Improved Kernel Partial Least Squares) Algorithms #1 and #2 [[1]](#references) for CPUs, GPUs, and TPUs. IKPLS is both fast [[2]](#references) and numerically stable [[3]](#references) making it optimal for PLS modeling.