Description:

The NaN-toolbox provides a number of statistics functions and machine learning methods for the use with Octave and Matlab. The functions can handle data with missing values encoded as NaNs, weighting of data samples, and multi-class classification (using a one-versus-rest scheme). There is a common interface to a number of different classification methods (including FDA, LDA, Naive Bayes, QDA, RDA, sparse classifiers, interfaces to some SVMs, regression/PLS, Wiener-Hopf).

Changes to previous version:

xptopen.mex: [HTML_REMOVED] reads and writes SAS Transport format (*.xpt) ttest and ttest2: [HTML_REMOVED] paired and unpaired t-test for data with missing values (useful for users of Octave, and for Matlab users without statistics toolbox)

• train_sc: bug fix when labels are single column {-1,1} format
• test_sc: improved docu for R.output

For more details see: http://biosig-consulting.com/matlab/NaN/CHANGELOG

BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: Matlab, Octave, Csv, Dlm, Sasxpt, Stata

Tags: Classification, Multi Class, Machine Learning, Missing Data, Statistics, Weighting

Archive: download here

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