Partial Least-Squares Algorithm for Categorical and Scalar Functional Data // Performs the Partial Least-Squares ('PLS') algorithm for functional data through the concept of active area integration. This approach builds upon the basis expansion methods for functional 'PLS' regression described in Aguilera et al. (2010) doi:10.1016/j.chemolab.2010.09.007. The package seamlessly handles both Scalar Functional Data ('SFD') and Categorical Functional Data ('CFD'), providing interpretable regression curves even for discrete state changes. It was developed during a PhD thesis between 'DECATHLON' and French research institute 'INRIA' 2022-2026. The 'SmoothPLS' method does not directly decompose the data into a basis; rather, it assumes the data is known as precisely as desired, and for every 'PLS' component, the weight functions are decomposed into the basis. For both single-state and multi-state 'CFD' as well as 'SFD', the algorithm is implemented for a scalar response. To provide a baseline, a naive 'PLS' method on time-value functions and standard Functional 'PLS' are also implemented.