Package: Factoshiny 2.4

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, Astrid Monge, Francois Husson

Factoshiny_2.4.tar.gz
Factoshiny_2.4.zip(r-4.5)Factoshiny_2.4.zip(r-4.4)Factoshiny_2.4.zip(r-4.3)
Factoshiny_2.4.tgz(r-4.4-any)Factoshiny_2.4.tgz(r-4.3-any)
Factoshiny_2.4.tar.gz(r-4.5-noble)Factoshiny_2.4.tar.gz(r-4.4-noble)
Factoshiny_2.4.tgz(r-4.4-emscripten)Factoshiny_2.4.tgz(r-4.3-emscripten)
Factoshiny.pdf |Factoshiny.html✨
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:

7.36 score 9 stars 132 scripts 4.8k downloads 8 mentions 14 exports 135 dependencies

Last updated 1 years agofrom:ebae1836c3. Checks:OK: 3 NOTE: 4. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 07 2024
R-4.5-win	NOTE	Nov 07 2024
R-4.5-linux	NOTE	Nov 07 2024
R-4.4-win	NOTE	Nov 07 2024
R-4.4-mac	NOTE	Nov 07 2024
R-4.3-win	OK	Nov 07 2024
R-4.3-mac	OK	Nov 07 2024

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 Deriv digest doBy doParallel dplyr DT ellipse emmeans estimability evaluate FactoInvestigate FactoMineR fansi farver fastmap flashClust fontawesome forcats foreach Formula fs generics ggplot2 ggrepel glmnet glue gtable haven highr hms htmltools htmlwidgets httpuv isoband iterators jomo jquerylib jsonlite knitr labeling later lattice lazyeval leaps lifecycle lme4 magrittr MASS Matrix MatrixModels memoise mgcv mice microbenchmark mime miniUI minqa missMDA mitml modelr multcompView munsell mvtnorm nlme nloptr nnet numDeriv ordinal pan pbkrtest pillar pkgconfig prettyunits progress promises purrr quantreg R6 rappdirs RColorBrewer Rcpp RcppEigen readr rlang rmarkdown rpart sass scales scatterplot3d shape shiny shinydashboard shinyjqui shinyjs sourcetools SparseM stringi stringr survival tibble tidyr tidyselect tinytex tzdb ucminf utf8 vctrs viridisLite vroom withr xfun xtable yaml

Factoshiny Introduction

Rendered fromFactoshiny.Rnwusingutils::Sweaveon Nov 07 2024.

Last update: 2019-01-15
Started: 2019-01-15

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 Factoshiny
Correspondance Analysis (CA) with Factoshiny	CAshiny
Categories description	catdesshiny
Continuous variable description	condesshiny
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)