Package: limpca 1.9.0
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_1.9.0.tar.gz
limpca_1.9.0.zip(r-4.7)limpca_1.9.0.zip(r-4.6)limpca_1.9.0.zip(r-4.5)
limpca_1.9.0.tgz(r-4.6-any)limpca_1.9.0.tgz(r-4.5-any)
limpca_1.9.0.tar.gz(r-4.7-any)limpca_1.9.0.tar.gz(r-4.6-any)
limpca_1.9.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
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
Pkgdown/docs site:https://manonmartin.github.io
On BioConductor:limpca-1.9.0(bioc 3.24)limpca-1.8.0(bioc 3.23)
statisticalmethodprincipalcomponentregressionvisualizationexperimentaldesignmultiplecomparisongeneexpressionmetabolomics
Last updated from:eb4fa56366. Checks:1 NOTE, 9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| bioc-checks | NOTE | 264 | ||
| linux-devel-x86_64 | OK | 380 | ||
| source / vignettes | OK | 352 | ||
| linux-release-x86_64 | OK | 372 | ||
| macos-release-arm64 | OK | 262 | ||
| macos-oldrel-arm64 | OK | 205 | ||
| windows-devel | OK | 313 | ||
| windows-release | OK | 327 | ||
| windows-oldrel | OK | 289 | ||
| wasm-release | OK | 180 |
Exports:data2LmpDataListlmpBootstrapTestslmpContributionslmpEffectMatriceslmpEffectPlotlmpLoading1dPlotlmpLoading2dPlotlmpModelMatrixlmpPcaEffectslmpScorePlotlmpScoreScatterPlotMlmpScreePlotpcaBySvdpcaLoading1dPlotpcaLoading2dPlotpcaScorePlotpcaScreePlotplotDesignplotLineplotMeansplotScatterplotScatterM
Dependencies:abindaskpassbackportsbase64encBiobaseBiocGenericsbitbit64blobbroombslibcachemcallrcellrangerclicliprcodetoolsconflictedcpp11crayoncurldata.tableDBIdbplyrDelayedArraydigestdoParalleldplyrdtplyrevaluatefarverfastmapfontawesomeforcatsforeachfsgarglegenericsGenomicRangesggplot2ggrepelggscigluegoogledrivegooglesheets4gtablehavenhighrhmshtmltoolshttridsIRangesisobanditeratorsjquerylibjsonliteknitrlabelinglatticelifecyclelubridatemagrittrMatrixMatrixGenericsmatrixStatsmemoisemimemodelropensslpillarpkgconfigplyrprettyunitsprocessxprogresspspurrrR6raggrappdirsRColorBrewerRcppreadrreadxlrematchrematch2reprexreshape2rlangrmarkdownrstudioapirvestS4ArraysS4VectorsS7sassscalesselectrSeqinfoSparseArraystringistringrSummarizedExperimentsyssystemfontstextshapingtibbletidyrtidyselecttidyversetimechangetinytextzdbutf8uuidvctrsviridisLitevroomwithrxfunxml2XVectoryaml
Application of limpca on the Trout transcriptomic dataset.
Rendered fromTrout.Rmdusingknitr::rmarkdownon Jun 03 2026.Last update: 2024-04-26
Started: 2022-12-07
Application of limpca on the UCH metabolomics dataset.
Rendered fromUCH.Rmdusingknitr::rmarkdownon Jun 03 2026.Last update: 2024-04-26
Started: 2022-09-20
Get started with limpca.
Rendered fromlimpca.Rmdusingknitr::rmarkdownon Jun 03 2026.Last update: 2024-04-26
Started: 2022-12-06
