Another, minor update of paper.md in relation to #29.

sm00thix · Jun 22, 2024 · 3fb1052 · 3fb1052
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 # Summary
-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. The PLS implementations in `ikpls` use the fast IKPLS (Improved Kernel PLS) algorithms [@dayal1997improved], providing a substantial speedup compared to scikit-learn's [@scikit-learn] PLS implementation, which is based on NIPALS (Nonlinear Iterative Partial Least Squares) [@wold1966estimation]. The `ikpls` package also offers an implementation of IKPLS combined with the fast cross-validation algorithms by Engstrøm [@engstrøm2024shortcutting], significantly accelerating cross-validation of PLS models - especially when using a large number of cross-validation splits.
+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. The PLS implementations in `ikpls` use the fast IKPLS (Improved Kernel PLS) algorithms [@dayal1997improved], providing a substantial speedup compared to scikit-learn's [@scikit-learn] PLS implementation, which is based on NIPALS (Nonlinear Iterative Partial Least Squares) [@wold1966estimation]. The `ikpls` package also offers an implementation of IKPLS combined with the fast cross-validation algorithm by Engstrøm [@engstrøm2024shortcutting], significantly accelerating cross-validation of PLS models - especially when using a large number of cross-validation splits.
 
 `ikpls` offers NumPy-based CPU and JAX-based CPU/GPU/TPU implementations. The JAX implementations are also differentiable, allowing seamless integration with deep learning techniques. This versatility enables users to handle diverse data dimensions efficiently.