Download the R-based chemometric software developed by the Group of Chemometrics of the Italian Chemical Society.

Note for the "old" users: with the updates dated April 6 2017 or following, the folder Perl.5.16 is no more required and can therefore be deleted

For any suggestion or bug please contact Riccardo Leardi (Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.)

Collection of Matlab modules for calculating unsupervised and supervised mutlivariate models (download from the Milano Chemometrics and QSAR Research Group):

  • Classification toolbox (for Matlab): classification (supervised pattern recognition) multivariate models: Discriminant Analysis, Partial Least Square Discriminant Analysis (PLSDA), Classification trees (CART), K-Nearest Neighbors (kNN), Potential Functions (Kernel Density Estimators), Support Vector Machines (SVM) and Soft Independent Modeling of Class Analogy (SIMCA).
  • PCA toolbox (for Matlab): unsupervised multivariate models for data structure analysis: Principal Component Analysis (PCA), Multidimensional Scaling (MDS) and Cluster Analysis.
  • N3-BNN toolbox (for Matlab): N3 (N-Nearest Neighbours), BNN (Binned Nearest Neighbours) and kNN (k Nearest Neighbours) local classification methods.
  • Kohonen and CPANN toolbox (for Matlab): Kohonen Maps and Counterpropagation Artificial Neural networs (CPANNs), Supervised Kohonen networks and XY-fused networks.
  • PLS-GA toolbox (for Matlab): Matlab modules for variable selection based on Genetic Algorithms coupled with PLS by Riccardo Leardi (download from the Quality & Technology website, Department of Food Science, University of Copenhagen)

The NSIMCA toolbox extends the SIMCA method to array of order >= 2.   NSIMCA is written in MATLAB and includes few essential Guidelines. The toolbox is available from: www.models.life.ku.dk/nsimca.

The reference for the NSIMCA toolbox for MATLAB is: C. Durante, R. Bro, M. Cocchi, A classification tool for N-way array based on SIMCA methodology, Chemometrics & Intelligent Laboratory Systems. 106 (2011), 73-85.

If you have any questions, suggestions or comments please feel free to mail us (Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.).

The software calculates and displays VIP (Variable Influence in Projection) scores for NPLS and NPLSDA models. The Multi-way VIP code is written in MATLAB and can be downloaded at: www.models.life.ku.dk/nvip

It is described in: S. Favilla C. Durante,M. Li Vigni,M. Cocchi, Assessing feature relevance in NPLS models by VIP, Chemom. Intell. Lab. Syst. 2013 (129) 76-86.

If you have any questions, suggestions or comments please feel free to mail us (Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.).