linkspotter1.3.0 package

Bivariate Correlations Calculation and Visualization

BeEFdiscretization.numfact

BeEF: Best Equal-Frequency discretization

BeEFdiscretization.numnum

BeEF: Best Equal-Frequency discretization (for a couple of quantitativ...

clusterVariables

Variable clustering (using Normal Mixture Modeling for Model-Based Clu...

corCouplesToMatrix

Couples to matrix

createShinyAppFolder

Ready-for-deployment shiny app folder creation

EFdiscretization

EF: Equal-Frequency discretization

is.not.informative.variable

Is a vector an non informative variable

linkspotterComplete

Linkspotter complete runner

linkspotterGraph

Linkspotter graph runner

linkspotterGraphOnMatrix

Linkspotter graph on matrix

linkspotterOnFile

Process Linkspotter on an external file

linkspotterUI

Linkspotter user interface runner

matrixToCorCouples

Matrix to couples

maxNMI

Maximal Normalized Mutual Information (MaxNMI)

multiBivariateCorrelation

Calculation of all the bivariate correlations in a dataframe

NormalizedMI

Maximal Normalized Mutual Information (MaxNMI) function for 2 categori...

Download source packageRead PDF manual

Compute and visualize using the 'visNetwork' package all the bivariate correlations of a dataframe. Several and different types of correlation coefficients (Pearson's r, Spearman's rho, Kendall's tau, distance correlation, maximal information coefficient and equal-freq discretization-based maximal normalized mutual information) are used according to the variable couple type (quantitative vs categorical, quantitative vs quantitative, categorical vs categorical).

  • Maintainer: Alassane Samba
  • License: MIT + file LICENSE
  • Last published: 2020-07-23

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