Extended README.md with the first to sentences of paper/paper.md. Als…

…o fixed some capitalization in paper/paper.md. This is related to #29

README.md

@@ -16,6 +16,8 @@
 
 [![JOSS Status](https://joss.theoj.org/papers/ac559cbcdc6e6551f58bb3e573a73afc/status.svg)](https://joss.theoj.org/papers/ac559cbcdc6e6551f58bb3e573a73afc)
 
+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.
+
 ## 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.

paper/paper.md

@@ -37,7 +37,7 @@ bibliography: paper.bib
 ---
 
 # 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 algorithm 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.