Marco Riani, Professor of Statistics

      Univ. of Parma (ITALY)



FSDA Toolbox extends MATLAB and statistics toolbox to support a robust and efficient analysis of complex data sets.

FSDA was first released on March 2012.

Last update: June  16, 2016



You can have a look at the toolbox features through a short video or through some didactic movies

Movie 1: Hawkins data

Movie 2: Fishery data

Movie 3: AR data

Movie 4: Loyalty cards data

Movie 5: Hospital data


Installation Notes

In the Installation Notes we tried to document all that you have to expect when FSDA is installed manually by unpacking the compressed tar file FSDA.tar.gz, or automatically with our setup program for Windows platforms.

Download FSDA A setup executable for MS Windows platforms will install the toolbox and update the search path of your local MATLAB installation.

Download a compressed tar file of the toolbox, suitable for Unix platforms installation. In this case, you have to add manually FSDA folder and sub-folders to the MATLAB path or use our routine addFSDA2path.

Download a working paper describing the main characteristics of the FSDA toolbox or see Riani, Perrotta, Torti (2012).


Highlights of the last releases

June 2016 Release (v. 3.2)

Major statistical release. Highlights:
  • New features added to the tclust function, including determinant restriction and new adjusted BIC criterion for the estimation of the number of groups.
  • Added functions for reweighting FSR and FSRB (FSRr and FSRBr).
  • Functions FSR, FSRB and FSRH redesigned; a routing implementing the core of the Forward Search algorithm (FSRcore) introduced to avoid code redundancies.
  • New function, winsor, to winsor data.
  • New function FSMbsb, which will replace FSMbbm.
  • New function randindexFS, to evaluate the quality of different clusterings.
  • New routines poolClose and poolPrepare introduced to conveniently open and close a pool of parallel workers.
  • Several new robust functions to generate, for example, the Tukey Biweigh rho function (HUrho), the tuning constant associated to a certain efficiency (HUeff), the psi functions (HUpsi), its derivative (HUpsider), etc. For a full list, see functions under utilities_stats folder.

Major highlights in the documentations and examples:

  • New function, publishFS, introduced to generate documentation pages directly from the .m files.
  • New function, makecontentsfileFS, introduced to create a the list of files present in a FSDA folder and/or subfolders. It extends MATLAB function makecontentsfile.
  • .mlx files introduced for examples_multivariate and examples_regression

September 2015 Release (v. 3.1)

  • Function simdataset.m modified to allow the user to simulate outliers from different distributions and contamination schemes and/or contaminate existing datasets.
  • New Bayesian regression analysis routines: FSRB.m, FSRBeda.m, FSRBmdr.m, regressB.m.
  • In FSReda.m: monitoring of confidence intervals of beta and sigma2.
  • In FSRBeda.m: monitoring of HPD (highest posterior density regions) of beta and sigma2.
  • New functions for inverse gamma computation: inversegampdf.m, inversegamcdf.m, inversegaminv.m.
  • Added functions to monitor units forming subset in heterosckedastic and Bayesian regression: FSRHbsb.m, FSRBbsb.m.
  • Added new datasets for Bayesian examples.
  • Added option for the robust transformation in the Yeo-Johnson family.
  • addFSDA2path.m: modified for compatibility with unix platforms and to address changes in the folder organization of FSDA functions.
  • Added routines to compute and visualize robust bivariate boxplot (function boxplotb.m).
  • New routine for automatic outlier detection in heteroskedastic regression (FSRH.m).

Please, do not hesitate to contact us for any bug you might find and for any suggestion you might have!!! Thanks to all those who have already contacted us and have helped us to correct several bugs and improve the performance of the code.

Marco Riani, Andrea Cerioli and Aldo Corbellini (University of Parma)

Domenico Perrotta and Francesca Torti (EC, Joint Research Centre)

Patrizia Calcaterra, Andrea Cerasa, Emmanuele Sordini, Daniele Palermo (EC, Joint Research Centre)