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:

• Default built is with OpenMP enabled

• mex binaries (*.mexw64) for 64 bit windows/matlab included

• *.mexw32 were built mit OpenMP enabled (increase speed on multi-core machines)

• minor issues: include Performance_test, cumsum_skipnan.

BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: Matlab, Arff, Octave, Csv, Dlm, Stata, Sas, Xpt

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

Archive: download here

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