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Пакет: r-cran-factominer (1.39-1)

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.

Други пакети, свързани с r-cran-factominer

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  • dep: r-api-3.4
    виртуален пакет, предлаган от r-base-core
  • dep: r-base-core (>= 3.4.3-1)
    GNU R core of statistical computation and graphics system
  • dep: r-cran-car
    GNU R Companion to Applied Regression by John Fox
  • dep: r-cran-cluster
    GNU R package for cluster analysis by Rousseeuw et al
  • dep: r-cran-ellipse
    GNU R functions for drawing ellipses and ellipse-like confidence regions
  • dep: r-cran-flashclust
    Implementation of optimal hierarchical clustering
  • dep: r-cran-lattice
    GNU R package for 'Trellis' graphics
  • dep: r-cran-leaps
    Regression Subset Selection
  • dep: r-cran-mass
    GNU R package of Venables and Ripley's MASS
  • dep: r-cran-scatterplot3d
    GNU R package for Visualizing Multivariate Data
  • sug: r-cran-knitr
    GNU R package for dynamic report generation using Literate Programming

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