Package: Factoshiny 2.8

Francois Husson

Factoshiny: Perform Factorial Analysis from 'FactoMineR' with a Shiny Application

Perform factorial analysis with a menu and draw graphs interactively thanks to 'FactoMineR' and a Shiny application.

Authors:Pauline Vaissie [aut], Astrid Monge [aut], Francois Husson [aut, cre]

Factoshiny_2.8.tar.gz
Factoshiny_2.8.zip(r-4.7)Factoshiny_2.8.zip(r-4.6)Factoshiny_2.8.zip(r-4.5)
Factoshiny_2.8.tgz(r-4.6-any)Factoshiny_2.8.tgz(r-4.5-any)
Factoshiny_2.8.tar.gz(r-4.7-any)Factoshiny_2.8.tar.gz(r-4.6-any)
Factoshiny_2.8.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
Factoshiny/json (API)

# Install 'Factoshiny' in R:

install.packages('Factoshiny', repos = c('https://husson.r-universe.dev', 'https://cloud.r-project.org'))

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

On CRAN:

6.57 score 9 stars 217 scripts 4.8k downloads 8 mentions 14 exports 157 dependencies

Last updated from:03528dbbda. Checks:9 OK. Indexed: yes.

Target	Result	Time	Files	Syslog
linux-devel-x86_64	OK	255
source / vignettes	OK	282
linux-release-x86_64	OK	257
macos-release-arm64	OK	133
macos-oldrel-arm64	OK	142
windows-devel	OK	163
windows-release	OK	132
windows-oldrel	OK	136
wasm-release	OK	190

Exports:CAshiny catdesshiny condesshiny Factoshiny FAMDshiny HCPCshiny MCAshiny MFAshiny PCAshiny print.CAshiny print.FAMDshiny print.HCPCshiny print.MCAshiny print.PCAshiny

Dependencies:abind backports base64enc bit bit64 boot broom bslib cachem car carData cli clipr cluster codetools colorspace colourpicker commonmark cowplot cpp11 crayon crosstalk curl Deriv digest doBy doParallel dplyr DT ellipse emmeans estimability evaluate FactoInvestigate FactoMineR farver fastmap flashClust fontawesome forcats foreach forecast Formula fracdiff fs generics ggplot2 ggrepel ggtext glmnet glue gridtext gtable haven highr hms htmltools htmlwidgets httpuv irlba isoband iterators jomo jpeg jquerylib jsonlite knitr labeling later lattice lazyeval leaps lifecycle litedown lme4 lmtest magrittr markdown MASS Matrix MatrixModels memoise mgcv mice microbenchmark mime miniUI minqa missMDA mitml modelr multcompView mvtnorm nlme nloptr nnet numDeriv ordinal otel pan pbkrtest pillar pkgconfig png prettyunits progress promises purrr quantreg R6 rappdirs rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack readr reformulas rlang rmarkdown rpart S7 sass scales scatterplot3d shape shiny shinydashboard shinyjqui shinyjs showtext showtextdb sourcetools SparseM stringi stringr survival sysfonts tibble tidyr tidyselect timeDate tinytex tzdb ucminf urca utf8 vctrs viridisLite vroom withr xfun xml2 xtable yaml zoo

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Perform classical factorial analysis from FactoMineR within a Shiny app ~~ Factoshiny ~~	Factoshiny-package
Correspondance Analysis (CA) with Factoshiny	CAshiny
Categories description	catdesshiny
Continuous variable description	condesshiny
Choose the Factoshiny function to perform the analysis	Factoshiny
Factor Analysis for Mixed Data with Factoshiny	FAMDshiny
Hierarchical Clustering on Principal Components (HCPC) with Factoshiny	HCPCshiny
Multiple Correspondence Analysis (MCA) with Factoshiny	MCAshiny
Multiple Factor Analysis (MFA) with Factoshiny	MFAshiny
Principal Component Analysis (PCA) with FactoShiny	PCAshiny
Print the CAshiny results	print.CAshiny
Print the FAMDshiny results	print.FAMDshiny
Print the HCPCshiny results	print.HCPCshiny
Print the MCAshiny results	print.MCAshiny
Print the MFAshiny results	print.MFAshiny
Print the PCAshiny results	print.PCAshiny

Usage by other packages (reverse dependencies)