{ "_id": "6a0f709eacfb0bcc41c5f543", "Package": "linkspotter", "Type": "Package", "Title": "Bivariate Correlations Calculation and Visualization", "Version": "1.4.0.9000", "Date": "2020-07-23", "Authors@R": "c(\nperson(\"Alassane\", \"Samba\", role = c(\"aut\", \"cre\"),email = \"alassane.samba@orange.com\"),\nperson(family=\"Orange\", role = c(\"cph\"))\n)", "Description": "Compute and visualize using the 'visNetwork' package all\nthe bivariate correlations of a dataframe. Several and\ndifferent types of correlation coefficients (Pearson's r,\nSpearman's rho, Kendall's tau, distance correlation, maximal\ninformation coefficient and equal-freq discretization-based\nmaximal normalized mutual information) are used according to\nthe variable couple type (quantitative vs categorical,\nquantitative vs quantitative, categorical vs categorical).", "License": "MIT + file LICENSE", "URL": "https://github.com/sambaala/linkspotter", "BugReports": "https://github.com/sambaala/linkspotter/issues", "Encoding": "UTF-8", "Collate": "'linkspotterComplete.R' 'clusterVariables.R'\n'linkspotterDurationEstimator.R' 'linkspotterGraph.R'\n'linkspotterGraphOnMatrix.R' 'linkspotterOnFile.R'\n'linkspotterUI.R' 'corCouplesToMatrix.R' 'matrixToCorCouples.R'\n'maxNMI.R' 'BeEFdiscretization_numfact.R'\n'BeEFdiscretization_numnum.R' 'multiBivariateCorrelation.R'\n'NormalizedMI.R' 'createShinyAppFolder.R' 'EFdiscretization.R'\n'non_informative_var.R'", "RoxygenNote": "7.1.1", 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discretization (for a couple of quantitative variables)", "topics": [ "BeEFdiscretization.numnum" ] }, { "page": "clusterVariables", "title": "Variable clustering (using Normal Mixture Modeling for Model-Based Clustering : mclust)", "topics": [ "clusterVariables" ] }, { "page": "corCouplesToMatrix", "title": "Couples to matrix", "topics": [ "corCouplesToMatrix" ] }, { "page": "createShinyAppFolder", "title": "Ready-for-deployment shiny app folder creation", "topics": [ "createShinyAppFolder" ] }, { "page": "EFdiscretization", "title": "EF: Equal-Frequency discretization", "topics": [ "EFdiscretization" ] }, { "page": "is.not.informative.variable", "title": "Is a vector an non informative variable", "topics": [ "is.not.informative.variable" ] }, { "page": "linkspotterComplete", "title": "Linkspotter complete runner", "topics": [ "linkspotterComplete" ] }, { "page": "linkspotterGraph", "title": "Linkspotter graph runner", "topics": [ "linkspotterGraph" ] }, { "page": "linkspotterGraphOnMatrix", "title": "Linkspotter graph on matrix", "topics": [ "linkspotterGraphOnMatrix" ] }, { "page": "linkspotterOnFile", "title": "Process Linkspotter on an external file", "topics": [ "linkspotterOnFile" ] }, { "page": "linkspotterUI", "title": "Linkspotter user interface runner", "topics": [ "linkspotterUI" ] }, { "page": "matrixToCorCouples", "title": "Matrix to couples", "topics": [ "matrixToCorCouples" ] }, { "page": "maxNMI", "title": "Maximal Normalized Mutual Information (MaxNMI)", "topics": [ "maxNMI" ] }, { "page": "multiBivariateCorrelation", "title": "Calculation of all the bivariate correlations in a dataframe", "topics": [ "multiBivariateCorrelation" ] }, { "page": "NormalizedMI", "title": "Maximal Normalized Mutual Information (MaxNMI) function for 2 categorical variables", "topics": [ "NormalizedMI" ] } ], "_readme": "https://github.com/orange-opensource/linkspotter/raw/HEAD/README.md", "_rundeps": [ "base64enc", "boot", "bslib", "cachem", "cli", 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