Title: Automatic Description of Factorial Analysis
Description: 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).
Imports: FactoMineR (>= 1.33), stats, methods, graphics, rrcov,

Imports: FactoMineR, stats, methods, graphics, rrcov, rmarkdown,

Imports: FactoMineR, stats, methods, graphics, rmarkdown, parallel,

Title: Factor Analysis and Data Mining with R
Author: Fran�ois Husson, S�bastien L�, J�r�my Mazet
Description: an R package for exploratory data analysis
Packaged: Mon Apr 10 13:22:10 2006; Husson

Packaged: Tue May 30 12:49:15 2006; Husson

Packaged: Fri Sep  8 13:52:53 2006; Husson

Packaged: Thu Feb 15 13:14:36 2007; hornik

Packaged: Mon Feb 26 09:00:33 2007; Fran�ois

Packaged: Fri Apr  6 15:20:39 2007; Fran�ois

Author: Francois Husson, Julie Josse, Sebastien Le, Jeremy Mazet
Packaged: Fri Oct  5 17:24:15 2007; francois

Packaged: Fri Feb 22 12:46:22 2008; francois

Packaged: Sat Jul 12 16:54:28 2008; francois

Packaged: Tue Sep 16 08:18:32 2008; francois

Packaged: Fri Apr 24 17:20:52 2009; francois

Title: Multivariate Exploratory Data Analysis and Data Mining with R

Description: Exploratory data analysis methods such as principal component methods and clustering

Title: Multivariate Exploratory Data Analysis and Data Mining

Description: Exploratory data analysis methods such as principal component methods and clustering.

Description: 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.