N-way Toolbox

swMATH ID: 12996
Software Authors: Andersson, C. A.; Bro, R.
Description: The N-way toolbox for MATLAB. The N-way toolbox provides means for: Fitting multi-way PARAFAC models; Fitting multi-way PLS regression models; Fitting multi-way Tucker models; Fitting the generalized rank annihilation method; Fitting the direct trilinear decomposition; Fitting models subject to constraints on the parameters such as e.g. nonnegativity, unimodality, orthogonality; Fitting models with missing values (using expectation maximization); Fitting models with a weighted least squares loss function (including MILES); Predicting scores for new samples using a given model; Predicting the dependent variable(s) of PLS models; Performing multi-way scaling and centering; Performing cross-validation of models; Calculating core consistency of PARAFAC models; Using additional diagnostic tools to evaluate the appropriate number of components; Perform rotations of core and models in Tucker models; Plus additional utility functions. In addition to the N-way toolbox, you can find a number of other multi-way tools on this site including PARAFAC2, Slicing (for exponential data such as low-res NMR), GEMANOVA for generalized multiplicative ANOVA, MILES for maximum likelihood fitting, conload for congruence and correlation loadings, eemscat for scatter handling of EEM data, clustering for multi-way clustering, CuBatch for batch data analysis, indafac for PARAFAC, PARALIND for constrained PARAFAC models, jackknifing for PARAFAC.
Homepage: http://www.models.life.ku.dk/nwaytoolbox
Dependencies: Matlab
Related Software: TensorToolbox; Matlab; Algorithm 862; Multilinear Engine; Python; PARAFAC; Silhouettes; Tensorlab; DFacTo; SPLATT; GigaTensor; LOBPCG; cross2D; TensorCalculus; TT Toolbox; htucker; ALPS; PLS_Toolbox; pandas; Scikit
Cited in: 29 Publications

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