parafac4microbiome - Parallel Factor Analysis Modelling of Longitudinal Microbiome Data
Creation and selection of PARAllel FACtor Analysis (PARAFAC) models of longitudinal microbiome data. You can import your own data with our import functions or use one of the example datasets to create your own PARAFAC models. Selection of the optimal number of components can be done using assessModelQuality() and assessModelStability(). The selected model can then be plotted using plotPARAFACmodel(). The Parallel Factor Analysis method was originally described by Caroll and Chang (1970) <doi:10.1007/BF02310791> and Harshman (1970) <https://www.psychology.uwo.ca/faculty/harshman/wpppfac0.pdf>.
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dimensionality-reductionmicrobiomemicrobiome-datamultiwaymultiway-algorithmsparallel-factor-analysis
7.24 score 9 stars 1 dependents 107 scripts 224 downloads
CMTFtoolbox - Create (Advanced) Coupled Matrix and Tensor Factorization Models
Creation and selection of (Advanced) Coupled Matrix and Tensor Factorization (ACMTF) and ACMTF-Regression (ACMTF-R) models. Selection of the optimal number of components can be done using 'ACMTF_modelSelection()' and 'ACMTFR_modelSelection()'. The CMTF and ACMTF methods were originally described by Acar et al., 2011 <doi:10.48550/arXiv.1105.3422> and Acar et al., 2014 <doi:10.1186/1471-2105-15-239>, respectively.
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5.94 score 1 stars 289 scripts 550 downloads
NPLStoolbox - N-Way Partial Least Squares Modelling of Multi-Way Data
Creation and selection of N-way Partial Least Squares (NPLS) models. Selection of the optimal number of components can be done using ncrossreg(). NPLS was originally described by Rasmus Bro, see <doi:10.1002/%28SICI%291099-128X%28199601%2910%3A1%3C47%3A%3AAID-CEM400%3E3.0.CO%3B2-C>.
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dimensionality-reductionmultiwaymultiway-algorithmsnwaypartial-least-squares
5.44 score 78 scripts 138 downloads