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parafac4microbiome  

Parallel Factor Analysis Modelling of Longitudinal Microbiome Data
View on CRAN: Click here


Download and install parafac4microbiome package within the R console
Install from CRAN:
install.packages("parafac4microbiome")

Install from Github:
library("remotes")
install_github("cran/parafac4microbiome")

Install by package version:
library("remotes")
install_version("parafac4microbiome", "1.3.3")



Attach the package and use:
library("parafac4microbiome")
Maintained by
Geert Roelof van der Ploeg
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-09-17
Latest Update: 2025-07-31
Description:
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>.
How to cite:
Geert Roelof van der Ploeg (2024). parafac4microbiome: Parallel Factor Analysis Modelling of Longitudinal Microbiome Data. R package version 1.3.3, https://cran.r-project.org/web/packages/parafac4microbiome. Accessed 02 Nov. 2026.
Previous versions and publish date:
(2026-08-21 07:47), 1.0.2 (2024-09-17 18:30), 1.0.3 (2024-09-24 17:00), 1.1.2 (2025-03-22 01:00), 1.2.1 (2025-05-20 09:50), 1.3.2 (2025-07-31 12:00)
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Complete documentation for parafac4microbiome
Functions, R codes and Examples using the parafac4microbiome R package
Full parafac4microbiome package functions and examples
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