FactoMineR - 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. F. Husson, S. Le and J. Pages (2017).
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15.22 score 58 stars 122 dependents 9.9k scripts 114k downloadsmissMDA - Handling Missing Values with Multivariate Data Analysis
Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA or MCA.
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8.76 score 3 stars 13 dependents 672 scripts 7.3k downloadsFactoshiny - 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.
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6.56 score 9 stars 192 scripts 5.3k downloadsSensoMineR - Sensory Data Analysis
Statistical Methods to Analyse Sensory Data. SensoMineR: A package for sensory data analysis. S. Le and F. Husson (2008).
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5.74 score 3 dependents 122 scripts 659 downloadsFactoInvestigate - Automatic Description of Factorial Analysis
Brings a set of tools to help and automatically realise the description of principal component analyses (from 'FactoMineR' functions). Detection of existing outliers, identification of the informative components, graphical views and dimensions description are performed threw dedicated functions. The Investigate() function performs all these functions in one, and returns the result as a report document (Word, PDF or HTML).
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5.61 score 1 stars 1 dependents 82 scripts 5.5k downloads