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>.
Version: 1.3.2 Depends: R (≥ 2.10) Imports: compositions, cowplot, doParallel, dplyr, foreach, ggplot2, ggpubr, lifecycle, magrittr, methods, multiway, parallel, pracma, rlang, rTensor, stats, tidyr Suggests: knitr, phyloseq, rmarkdown, TreeSummarizedExperiment (≥ 2.16.1), SummarizedExperiment (≥ 1.38.1), testthat (≥ 3.0.0), withr, NPLStoolbox Published: 2025-07-31 DOI: 10.32614/CRAN.package.parafac4microbiome Author: Geert Roelof van der Ploeg [aut, cre], Johan Westerhuis [ctb], Anna Heintz-Buschart [ctb], Age Smilde [ctb], University of Amsterdam [cph, fnd] Maintainer: Geert Roelof van der Ploeg <g.r.ploeg at uva.nl> BugReports: https://github.com/GRvanderPloeg/parafac4microbiome/issues License: MIT + file LICENSE URL: https://grvanderploeg.com/parafac4microbiome/, https://github.com/GRvanderPloeg/parafac4microbiome/ NeedsCompilation: no Materials: README, NEWS CRAN checks: parafac4microbiome results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=parafac4microbiome to link to this page.
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