2026-10-15T13:20:50Z https://data.mendeley.com/oai

oai:data.mendeley.com/9x9h7fr4kn.9 2023-03-31T14:14:03Z

van Kollenburg, Geert Data, software and scripts related to the Process PLS methodology manuscript Mendeley Data Process PLS repository including software implentations, data and scripts. Repository contains: - R package pathmodelr (See README.txt for installation guide) (GPLv3) - Matlab library - Link to Python implementation (here: https://doi.org/10.5281/zenodo.7074754) - Simulated data of a crude oil distillation process (with scripts to reproduce the results) (CC-BY) - R code for the Val de Loire wine tasting analysis (CC-BY) New in v2: PDF with helpfile to understand the model output New in v4: Matlab implementation New in v8: - Val de Loir analysis file includes function to rename the Process PLS output in R and a function to provide the outer-model R2 values of each block in a simple manner. Also added the renaming function separately to 'software folder' New in V9: - Python Implementation (link: https://doi.org/10.5281/zenodo.7074754) Process PLS is a path modelling algorithm for multiblock data. First described in: van Kollenburg et al. Process PLS: Incorporating substantive knowledge into the predictive modelling of multiblock, multistep, multidimensional and multicollinear process data" https://doi.org/10.1016/j.compchemeng.2021.107466 Marketing Research Process Control Crude Oil Distillation Chemometrics Wine Analytical Chemistry Data Analysis Process Analytical Technology Chemometrics Software Taste Panel Offermans, Tim Bouman, Roel Teng, Sin Yong Jansen, Jeroen Postma, Geert Dataset doi:10.17632/9x9h7fr4kn.9 oai:data.mendeley.com/9x9h7fr4kn.9 GNU Public License Version 3 http://www.gnu.org/licenses/gpl-3.0.en.html https://data.mendeley.com/datasets/9x9h7fr4kn 2023-03-31T14:14:03Z