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Updated 26 Nov 2022
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.6.2
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.6.1
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.6.0
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.38
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.37
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.36
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.35
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.34
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.33
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.31
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.30
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.29
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.28
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.27
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.26
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.25
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.24
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.23
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.22
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.21
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.20
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.19
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.18
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.17
See release notes for this release on GitHub: https://github.com/UniprJRC/FSDA/releases/tag/8.5.16
New dataset car added to FSDA
New option to add colorbar associated to labels in function CorAnaplot
Improved documentation in the .html associated help files
Improved capabilities in ellipse.m. See
Added version control inside function mdpattern to cope with previous versions of MATLAB
New function mdpattern which finds and plots missing data patterns. For more details see http://rosa.unipr.it/FSDA/mdpattern.html
New options inside LTSts and simulateTS that allow a customized definition of the autoregressive component
New function mcdeda which monitors the output of mcd for a sequence of values of breakdown point
New cluster analysis datasets added
FSDA release 2021A fully tested
Added a series of GUIs to show the necessary calculations to obtain of a series of statistical indexes. For more information see for example:
Added new function barVariableWidth which produces a bar plot with different widths and colors for each bar. For a preview see http://rosa.unipr.it/FSDA/barVariableWidth.html.
Added a series of routines for the estimation of integrated and instantaneous variance of a diffusion process via Fourier analysis [Mancino, Recchioni, Sanfelici, ``Fourier-Malliavin Volatility Estimation. Theory and Practice'', 2017, Springer NY
New routines for robust correspondence analysis added
Option commonslope added to tclustreg, tclustregeda and tclustregIC (see for example http://rosa.unipr.it/FSDA/tclustreg.html) . Fixed minor bugs.
New function biplotFS which calls biplotAPP to create the dynamic boxplot (for a preview of the help of this function see http://rosa.unipr.it/FSDA/biplotFS.html). Improvements in function pcaFS (see http://rosa.unipr.it/FSDA/pcaFS.html)
Added option tag in FSR.m in order to tag the plots which are produced.
Improvements in functions CorAna and CorAnaplot. New dataset citiesItaly added
New function pcaFS and new app for dynamic biplot (prerelease version)
FSDA 2020b. See release_notes.html for the details of the new version
New function scatterboxplot which creates scatter diagram with marginal boxplots
resubmitted version due to connection problems
Corrected some typos in the documentation
New function waterfallchart.m to create waterfall charts (see https://en.wikipedia.org/wiki/Waterfall_chart). Function funnelplot.m renamed funnelchart.m
New function funnelplot to create funnel charts
Corrected minor bug in function tclustregeda
Added new regression clustering datasets. More details in the release_notes.html file
Updated file readme.md
First release where installation file has been created programmatically.
New upload due to connection problems
Corrected small bug in file docrootFS
See https://github.com/UniprJRC/FSDA/blob/master/helpfiles/FSDA/release_notes.html for the details or file release_notes.html inside the toolbox
Procedure for copying html help files made easier.
New functions for Power divergence, VIOM model and simulateLM.
Function LTStsVarSel.m now extends variable selection to AR components. File simulateTS.m does not need anymore the Econometrics toolbox.
Solved minor bug in function mcd
Apps images bug solved. Improved version of some html pages.
startup.m removed. Output of the examples of HTML files now available for downloading. More details in getting_started.mlx page.
HTML pointers files inside (FSDAroot)/helpfiles/pointersHTML regenerated.
License files updated
Function rescale renamed rescaledFS because in conflict with function rescale of MATLAB. FSDA dataset hospital renamed hospitalFS because it shadowed MATLAB dataset hospital. Thank you for the suggestions.
Deleted space in the installation folder
Improved file GettingStarted.mlx
This project hosts the source code to the original MATLAB FileExchange project and is place of active development.
FSDA Toolbox™ provides statisticians, engineers, scientists, researchers, financial analysts with a comprehensive set of tools to assess and understand their data. Flexible Statistics Data Analysis Toolbox™ software includes functions and interactive tools for analyzing and modeling data, learning and teaching statistics.
The Flexible Statistics Data Analysis Toolbox™ supports a set of routines to develop robust and efficient analysis of complex data sets (multivariate, regression, clustering, ...), ensuring an output unaffected by anomalies or deviations from model assumptions.
In addition, it offers a rich set interactive graphical tools which enable us to explore the connection in the various features of the different forward plots.
All Flexible Statistics Data Analysis Toolbox™ functions are written in the open MATLAB® language. This means that you can inspect the algorithms, modify the source code, and create your own custom functions.
For the details about the functions present in FSDA you can browse the categorial and alphabetical list of functions of the toolbox inside MATLAB (once FSDA is installed) or at the web addresses http://rosa.unipr.it/FSDA/function-cate.html and http://rosa.unipr.it/FSDA/function-alpha.html
- Is especially useful in detecting in data potential anomalies (outliers), even when they occur in groups. Can be used to identify sub-groups in heterogeneous data.
- Extends functionalities in key statistical domains requiring robust analysis (cluster analysis, discriminant analysis, model selection, data transformation).
- Integrates instruments for interactive data visualization and modern exploratory data analysis, designed to simplify the interpretation of the statistical results by the end user.
- Provides statisticians, engineers, scientists, financial analysts a comprehensive set of tools to assess and understand their data.
- Provides practitioners, students and teachers with functions and graphical tools for modeling complex data, learning and teaching statistics.
FSDA is developed for wide applicability. For its capacity to address problems focusing on anomalies in the data, it is expected that it will be used in applications such as anti-fraud, detection of computer network intrusions, e-commerce and credit cards frauds, customer and market segmentation, detection of spurious signals in data acquisition systems, in chemometrics (a wide field covering biochemistry, medicine, biology and chemical engineering), in issues related to the production of official statistics (e.g. imputation and data quality checks), and so on.
For more information see the Wiki page at https://github.com/UniprJRC/FSDA/wiki
- Run the examples contained in files examples_regression.m or examples_multivariate.m or examples_categorical.m. Notice that all examples are organized in cells
- Run the GUIs in the FSDA Matlab help pages.
For a preview see http://rosa.unipr.it/FSDA/examples.html
Watch the videos in the Examples section of the FSDA Matlab help pages For a preview see http://rosa.unipr.it/fsda_video.html
Read section "Introduction to robust statistics" or "Technical introduction to Robust Statistics" in the FSDA Matlab help pages. For a preview see http://rosa.unipr.it/FSDA/tutorials.html
Marco Riani (2022). FSDA (https://github.com/UniprJRC/FSDA/releases/tag/8.6.2), GitHub. Retrieved .
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