Package: parafac4microbiome 1.1.2.9000

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>.
Authors:
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parafac4microbiome.pdf |parafac4microbiome.html✨
parafac4microbiome/json (API)
NEWS
# Install 'parafac4microbiome' in R: |
install.packages('parafac4microbiome', repos = c('https://grvanderploeg.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/grvanderploeg/parafac4microbiome/issues
Pkgdown site:https://grvanderploeg.com
- Fujita2023 - Fujita2023 longitudinal microbiome data
- Shao2019 - Shao2019 longitudinal microbiome data
- vanderPloeg2024 - VanderPloeg2024 longitudinal dataset
dimensionality-reductionmicrobiomemicrobiome-datamultiwaymultiway-algorithmsparallel-factor-analysis
Last updated 8 hours agofrom:fb21edef63. Checks:9 OK. Indexed: yes.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Mar 29 2025 |
R-4.5-win | OK | Mar 29 2025 |
R-4.5-mac | OK | Mar 29 2025 |
R-4.5-linux | OK | Mar 29 2025 |
R-4.4-win | OK | Mar 29 2025 |
R-4.4-mac | OK | Mar 29 2025 |
R-4.4-linux | OK | Mar 29 2025 |
R-4.3-win | OK | Mar 29 2025 |
R-4.3-mac | OK | Mar 29 2025 |
Exports:%>%assessModelQualityassessModelStabilitycalculateFMScalculateSparsitycalculateVarExpcalcVarExpPerComponentcorcondiafac_to_vectflipLoadingsimportMicrobiotaProcessimportPhyloseqimportTreeSummarizedExperimentinitializePARAFACmultiwayCentermultiwayCLRmultiwayScaleparafacparafac_core_alsparafac_funparafac_gradientplotModelMetricplotModelStabilityplotModelTCCsplotPARAFACmodelprocessDataCubereinflateFacreinflateTensorreshapeDatasortComponentstransformPARAFACloadingsvect_to_fac
Dependencies:abindbackportsbayesmbootbroomcarcarDatacliCMLScodetoolscolorspacecompositionscorrplotcowplotcpp11DEoptimRDerivdoBydoParalleldplyrfansifarverforeachFormulagenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtableisobanditeratorslabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamizemodelrmultiwaymunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpolynompracmapurrrquadprogquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangrobustbaserstatixrTensorscalesSparseMstringistringrsurvivaltensorAtibbletidyrtidyselectutf8vctrsviridisLitewithr
Fujita2023_analysis
Rendered fromFujita2023_analysis.Rmd
usingknitr::rmarkdown
on Mar 29 2025.Last update: 2025-03-21
Started: 2024-03-08
Introduction to PARAFAC modelling
Rendered fromPARAFAC_introduction.Rmd
usingknitr::rmarkdown
on Mar 29 2025.Last update: 2025-03-21
Started: 2024-03-08
Shao2019_analysis
Rendered fromShao2019_analysis.Rmd
usingknitr::rmarkdown
on Mar 29 2025.Last update: 2025-03-21
Started: 2024-03-08
vanderPloeg2024_analysis
Rendered fromvanderPloeg2024_analysis.Rmd
usingknitr::rmarkdown
on Mar 29 2025.Last update: 2025-03-21
Started: 2024-03-08