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grvanderploeg
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>.
Maintained by Geert Roelof van der Ploeg. Last updated 21 hours ago.
dimensionality-reductionmicrobiomemicrobiome-datamultiwaymultiway-algorithmsparallel-factor-analysis
6 stars 6.15 score 13 scripts
cran
multiway:Component Models for Multi-Way Data
Fits multi-way component models via alternating least squares algorithms with optional constraints. Fit models include N-way Canonical Polyadic Decomposition, Individual Differences Scaling, Multiway Covariates Regression, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.
Maintained by Nathaniel E. Helwig. Last updated 6 years ago.
3 stars 2.73 score 6 dependents