Package: limpca 1.3.0
limpca: An R package for the linear modeling of high-dimensional designed data based on ASCA/APCA family of methods
This package has for objectives to provide a method to make Linear Models for high-dimensional designed data. limpca applies a GLM (General Linear Model) version of ASCA and APCA to analyse multivariate sample profiles generated by an experimental design. ASCA/APCA provide powerful visualization tools for multivariate structures in the space of each effect of the statistical model linked to the experimental design and contrarily to MANOVA, it can deal with mutlivariate datasets having more variables than observations. This method can handle unbalanced design.
Authors:
limpca_1.3.0.tar.gz
limpca_1.3.0.zip(r-4.5)limpca_1.3.0.zip(r-4.4)limpca_1.3.0.zip(r-4.3)
limpca_1.3.0.tgz(r-4.4-any)limpca_1.3.0.tgz(r-4.3-any)
limpca_1.3.0.tar.gz(r-4.5-noble)limpca_1.3.0.tar.gz(r-4.4-noble)
limpca_1.3.0.tgz(r-4.4-emscripten)limpca_1.3.0.tgz(r-4.3-emscripten)
limpca.pdf |limpca.html✨
limpca/json (API)
NEWS
# Install 'limpca' in R: |
install.packages('limpca', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/manonmartin/limpca/issues
On BioConductor:limpca-1.3.0(bioc 3.21)limpca-1.2.0(bioc 3.20)
statisticalmethodprincipalcomponentregressionvisualizationexperimentaldesignmultiplecomparisongeneexpressionmetabolomics
Last updated 23 days agofrom:8e4f4cef0d. Checks:OK: 3 WARNING: 4. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 04 2024 |
R-4.5-win | WARNING | Nov 04 2024 |
R-4.5-linux | WARNING | Nov 04 2024 |
R-4.4-win | WARNING | Nov 04 2024 |
R-4.4-mac | OK | Nov 04 2024 |
R-4.3-win | WARNING | Nov 04 2024 |
R-4.3-mac | OK | Nov 04 2024 |
Exports:data2LmpDataListlmpBootstrapTestslmpContributionslmpEffectMatriceslmpEffectPlotlmpLoading1dPlotlmpLoading2dPlotlmpModelMatrixlmpPcaEffectslmpScorePlotlmpScoreScatterPlotMlmpScreePlotpcaBySvdpcaLoading1dPlotpcaLoading2dPlotpcaScorePlotpcaScreePlotplotDesignplotLineplotMeansplotScatterplotScatterM
Dependencies:abindaskpassbackportsbase64encBiobaseBiocGenericsbitbit64blobbroombslibcachemcallrcellrangerclicliprcodetoolscolorspaceconflictedcpp11crayoncurldata.tableDBIdbplyrDelayedArraydigestdoParalleldplyrdtplyrevaluatefansifarverfastmapfontawesomeforcatsforeachfsgarglegenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2ggrepelggscigluegoogledrivegooglesheets4gtablehavenhighrhmshtmltoolshttridsIRangesisobanditeratorsjquerylibjsonliteknitrlabelinglatticelifecyclelubridatemagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemgcvmimemodelrmunsellnlmeopensslpillarpkgconfigplyrprettyunitsprocessxprogresspspurrrR6raggrappdirsRColorBrewerRcppreadrreadxlrematchrematch2reprexreshape2rlangrmarkdownrstudioapirvestS4ArraysS4VectorssassscalesselectrSparseArraystringistringrSummarizedExperimentsyssystemfontstextshapingtibbletidyrtidyselecttidyversetimechangetinytextzdbUCSC.utilsutf8uuidvctrsviridisLitevroomwithrxfunxml2XVectoryamlzlibbioc
Application of limpca on the Trout transcriptomic dataset.
Rendered fromTrout.Rmd
usingknitr::rmarkdown
on Nov 04 2024.Last update: 2024-04-26
Started: 2022-12-07
Application of limpca on the UCH metabolomics dataset.
Rendered fromUCH.Rmd
usingknitr::rmarkdown
on Nov 04 2024.Last update: 2024-04-26
Started: 2022-09-20
Get started with limpca.
Rendered fromlimpca.Rmd
usingknitr::rmarkdown
on Nov 04 2024.Last update: 2024-04-26
Started: 2022-12-06