Package: FactoMineR 2.12

Francois Husson

FactoMineR: Multivariate Exploratory Data Analysis and Data Mining

Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).

Authors:Francois Husson, Julie Josse, Sebastien Le, Jeremy Mazet

FactoMineR_2.12.tar.gz
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FactoMineR_2.12.tgz(r-4.4-x86_64)FactoMineR_2.12.tgz(r-4.4-arm64)FactoMineR_2.12.tgz(r-4.3-x86_64)FactoMineR_2.12.tgz(r-4.3-arm64)
FactoMineR_2.12.tar.gz(r-4.5-noble)FactoMineR_2.12.tar.gz(r-4.4-noble)
FactoMineR_2.12.tgz(r-4.4-emscripten)FactoMineR_2.12.tgz(r-4.3-emscripten)
FactoMineR.pdf |FactoMineR.html
FactoMineR/json (API)
NEWS

# Install 'FactoMineR' in R:
install.packages('FactoMineR', repos = c('https://husson.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/husson/factominer/issues

Datasets:

On CRAN:

14.76 score 42 stars 107 packages 5.0k scripts 107k downloads 1.1k mentions 71 exports 98 dependencies

Last updated 4 months agofrom:c7db3638cc. Checks:OK: 7 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 13 2024
R-4.5-win-x86_64NOTENov 13 2024
R-4.5-linux-x86_64NOTENov 13 2024
R-4.4-win-x86_64OKNov 13 2024
R-4.4-mac-x86_64OKNov 13 2024
R-4.4-mac-aarch64OKNov 13 2024
R-4.3-win-x86_64OKNov 13 2024
R-4.3-mac-x86_64OKNov 13 2024
R-4.3-mac-aarch64OKNov 13 2024

Exports:AovSumautoLabCACaGaltcatdescoeffRVcondescoord.ellipsedescfreqdimdescDMFAellipseCAestim_ncpFAMDGPAgraph.varHCPCHMFALinearModelMCAmeansCompMFAPCAplot.CAplot.CaGaltplot.catdesplot.DMFAplot.FAMDplot.GPAplot.HCPCplot.HMFAplot.MCAplot.meansCompplot.MFAplot.PCAplotellipsesplotGPApartialplotMFApartialpredict.CApredict.FAMDpredict.MCApredict.MFApredict.PCAprefplsprint.AovSumprint.CAprint.CaGaltprint.catdesprint.condesprint.FAMDprint.GPAprint.HCPCprint.HMFAprint.LinearModelprint.MCAprint.MFAprint.PCAreconstRegBestsimulesummary.CAsummary.CaGaltsummary.FAMDsummary.MCAsummary.MFAsummary.PCAsvd.triplettab.disjonctiftab.disjonctif.proptextualwrite.infile

Dependencies:abindbackportsbase64encbootbroombslibcachemcarcarDatacliclustercolorspacecowplotcpp11crosstalkDerivdigestdoBydplyrDTellipseemmeansestimabilityevaluatefansifarverfastmapflashClustfontawesomeFormulafsgenericsggplot2ggrepelgluegtablehighrhtmltoolshtmlwidgetshttpuvisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevalleapslifecyclelme4magrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqamodelrmultcompViewmunsellmvtnormnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpromisespurrrquantregR6rappdirsRColorBrewerRcppRcppEigenrlangrmarkdownsassscalesscatterplot3dSparseMstringistringrsurvivaltibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml

clustering

Rendered fromclustering.Rmdusingknitr::knitron Nov 13 2024.

Last update: 2023-10-26
Started: 2019-01-15

FactoMineR

Rendered fromFactoMineR.Rmdusingknitr::knitron Nov 13 2024.

Last update: 2023-10-26
Started: 2019-01-15

Readme and manuals

Help Manual

Help pageTopics
Multivariate Exploratory Data Analysis and Data Mining with RFactoMineR-package FactoMineR
Analysis of variance with the contrasts sum (the sum of the coefficients is 0)AovSum
Function to better position the labels on the graphsautoLab
Correspondence Analysis (CA)CA
Correspondence Analysis on Generalised Aggregated Lexical Table (CaGalt)CaGalt
Categories descriptioncatdes
Children (data)children
Calculate the RV coefficient and test its significancecoeffRV
Continuous variable descriptioncondes
Construct confidence ellipsescoord.ellipse
Performance in decathlon (data)decathlon
Description of frequenciesdescfreq
Dimension descriptiondimdesc
Dual Multiple Factor Analysis (DMFA)DMFA
Draw confidence ellipses in CAellipseCA
Estimate the number of components in Principal Component Analysisestim_ncp
Factor Analysis for Mixed DataFAMD
footsizefootsize
geomorphology(data)geomorphology
Generalised Procrustes AnalysisGPA
Make graph of variablesgraph.var
Hierarchical Clustering on Principle Components (HCPC)HCPC
health (data)health
Hierarchical Multiple Factor AnalysisHMFA
hobbies (data)hobbies
Number of medals in athletism during olympic games per countryJO
Linear Model with AIC or BIC selection, and with the contrasts sum (the sum of the coefficients is 0) if any categorical variablesLinearModel
Multiple Correspondence Analysis (MCA)MCA
Perform pairwise means comparisonsmeansComp
Multiple Factor Analysis (MFA)MFA
milkmilk
The cause of mortality in France in 1979 and 2006mortality
Principal Component Analysis (PCA)PCA
Draw the Correspondence Analysis (CA) graphsplot.CA
Draw the Correspondence Analysis on Generalised Aggregated Lexical Table (CaGalt) graphsplot.CaGalt
Plots for description of clusters (catdes)plot.catdes
Draw the Dual Multiple Factor Analysis (DMFA) graphsplot.DMFA
Draw the Multiple Factor Analysis for Mixt Data graphsplot.FAMD
Draw the General Procrustes Analysis (GPA) mapplot.GPA
Plots for Hierarchical Classification on Principle Components (HCPC) resultsplot.HCPC
Draw the Hierarchical Multiple Factor Analysis (HMFA) graphsplot.HMFA
Draw the Multiple Correspondence Analysis (MCA) graphsplot.MCA
Draw the means comparisonsplot.meansComp
Draw the Multiple Factor Analysis (MFA) graphsplot.MFA
Draw the Principal Component Analysis (PCA) graphsplot.PCA
Draw confidence ellipses around the categoriesplotellipses
Draw an interactive General Procrustes Analysis (GPA) mapplotGPApartial
Plot an interactive Multiple Factor Analysis (MFA) graphplotMFApartial
Poisonpoison
Poisonpoison.text
Genomic data for chickenpoulet
Predict projection for new rows with Correspondence Analysispredict.CA
Predict projection for new rows with Factor Analysis of Mixed Datapredict.FAMD
Predict method for Linear Model Fitspredict.LinearModel
Predict projection for new rows with Multiple Correspondence Analysispredict.MCA
Predict projection for new rows with Multiple Factor Analysispredict.MFA
Predict projection for new rows with Principal Component Analysispredict.PCA
Scatter plot and additional variables with quality of representation contour linesprefpls
Print the AovSum resultsprint.AovSum
Print the Correspondance Analysis (CA) resultsprint.CA
Print the Correspondence Analysis on Generalised Aggregated Lexical Table (CaGalt) resultsprint.CaGalt
Print the catdes resultsprint.catdes
Print the condes resultsprint.condes
Print the Multiple Factor Analysis of mixt Data (FAMD) resultsprint.FAMD
Print the Generalized Procrustes Analysis (GPA) resultsprint.GPA
Print the Hierarchical Clustering on Principal Components (HCPC) resultsprint.HCPC
Print the Hierarchical Multiple Factor Analysis resultsprint.HMFA
Print the LinearModel resultsprint.LinearModel
Print the Multiple Correspondance Analysis (MCA) resultsprint.MCA
Print the Multiple Factor Analysis resultsprint.MFA
Print the Principal Component Analysis (PCA) resultsprint.PCA
Reconstruction of the data from the PCA, CA or MFA resultsreconst
Select variables in multiple linear regressionRegBest
sensosenso
Simulate by bootstrapsimule
Printing summeries of ca objectssummary.CA
Printing summaries of CaGalt objectssummary.CaGalt
Printing summeries of FAMD objectssummary.FAMD
Printing summeries of MCA objectssummary.MCA
Printing summaries of MFA objectssummary.MFA
Printing summeries of PCA objectssummary.PCA
Singular Value Decomposition of a Matrixsvd.triplet
Make a disjonctif tabletab.disjonctif
Make a disjunctive table when missing values are presenttab.disjonctif.prop
tea (data)tea
Text miningtextual
Winewine
Print in a filewrite.infile