ExPosition-package | R Documentation |
Exposition is defined as a comprehensive explanation of an idea. With
ExPosition for R, a comprehensive explanation of your data will be provided
with minimal effort.
The core of ExPosition is the singular value
decomposition (SVD; see: svd
). The point of ExPosition is
simple: to provide the user with an overview of their data that only the SVD
can provide. ExPosition includes several techniques that depend on the SVD
(see below for examples and functions).
Questions, comments, compliments, and complaints go to Derek Beaton
exposition.software@gmail.com.
The following people are authors or contributors to ExPosition code, data,
or examples:
Derek Beaton, Hervé Abdi, Cherise Chin-Fatt, Joseph Dunlop,
Jenny Rieck, Rachel Williams, Anjali Krishnan, and Francesca M. Filbey.
Abdi, H., and Williams, L.J. (2010). Principal component
analysis. Wiley Interdisciplinary Reviews: Computational Statistics,
2, 433-459.
Abdi, H. and Williams, L.J. (2010). Correspondence analysis.
In N.J. Salkind, D.M., Dougherty, & B. Frey (Eds.): Encyclopedia of
Research Design. Thousand Oaks (CA): Sage. pp. 267-278.
Abdi, H. (2007).
Singular Value Decomposition (SVD) and Generalized Singular Value
Decomposition (GSVD). In N.J. Salkind (Ed.): Encyclopedia of
Measurement and Statistics.Thousand Oaks (CA): Sage. pp. 907-912.
Abdi,
H. (2007). Metric multidimensional scaling. In N.J. Salkind (Ed.):
Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage.
pp. 598-605.
Greenacre, M. J. (2007). Correspondence Analysis in
Practice. Chapman and Hall.
Benzécri, J. P. (1979). Sur le calcul
des taux d'inertie dans l'analyse d'un questionnaire. Cahiers de
l'Analyse des Données, 4, 377-378.
epPCA
, epMDS
, epCA
,
epMCA
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