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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>.

Versions across snapshots

VersionRepositoryFileSize
1.3.2 rolling linux/jammy R-4.5 parafac4microbiome_1.3.2.tar.gz 2.7 MiB
1.3.2 rolling linux/noble R-4.5 parafac4microbiome_1.3.2.tar.gz 2.7 MiB
1.3.2 rolling source/ R- parafac4microbiome_1.3.2.tar.gz 2.1 MiB
1.3.2 latest linux/jammy R-4.5 parafac4microbiome_1.3.2.tar.gz 2.7 MiB
1.3.2 latest linux/noble R-4.5 parafac4microbiome_1.3.2.tar.gz 2.7 MiB
1.3.2 latest source/ R- parafac4microbiome_1.3.2.tar.gz 2.1 MiB
1.3.2 2026-04-26 source/ R- parafac4microbiome_1.3.2.tar.gz 2.1 MiB
1.3.2 2026-04-23 source/ R- parafac4microbiome_1.3.2.tar.gz 2.1 MiB
1.3.2 2026-04-09 windows/windows R-4.5 parafac4microbiome_1.3.2.zip 2.7 MiB
1.1.2 2025-04-20 source/ R- parafac4microbiome_1.1.2.tar.gz 2.2 MiB

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