Package: NominalLogisticBiplot
Type: Package
Title: Biplot representations of categorical data
Version: 0.1
Date: 2013-09-09
Author: Julio Cesar Hernandez Sanchez, Jose Luis Vicente-Villardon
Maintainer: Julio Cesar Hernandez Sanchez <juliocesar_avila@usal.es>
Description: Analysis of a matrix of polytomous items using Nominal Logistic Biplots (NLB)
according to Hernandez-Sanchez and Vicente-Villardon (2013).
The NLB procedure extends the binary logistic biplot to nominal (polytomous) data.
The  individuals are represented as points on a plane and the  variables are represented
as convex prediction regions rather than vectors as in a classical or binary biplot.
Using the methods from Computational Geometry, the set of prediction regions is converted to a set of points
in such a way that the prediction for each individual is established by its closest
"category point". Then interpretation is based on distances rather than on projections.
In this package we implement the geometry of such a representation and construct computational algorithms
for the estimation of parameters and the calculation of prediction regions.
License: GPL (>= 2)
Encoding: latin1
Repository: CRAN
Depends: R (>= 2.15.1),mirt,gmodels,MASS
LazyData: yes
Archs: i386, x64
NeedsCompilation: no
Packaged: 2013-09-16 14:38:51 UTC; INE
Date/Publication: 2013-09-17 14:47:55

Package: NominalLogisticBiplot
Type: Package
Title: Biplot representations of categorical data
Version: 0.2
Date: 2014-05-01
Author: Julio Cesar Hernandez Sanchez, Jose Luis Vicente-Villardon
Maintainer: Julio Cesar Hernandez Sanchez <juliocesar_avila@usal.es>
Description: Analysis of a matrix of polytomous items using Nominal Logistic Biplots (NLB)
according to Hernandez-Sanchez and Vicente-Villardon (2013).
The NLB procedure extends the binary logistic biplot to nominal (polytomous) data.
The  individuals are represented as points on a plane and the  variables are represented
as convex prediction regions rather than vectors as in a classical or binary biplot.
Using the methods from Computational Geometry, the set of prediction regions is converted to a set of points
in such a way that the prediction for each individual is established by its closest
"category point". Then interpretation is based on distances rather than on projections.
In this package we implement the geometry of such a representation and construct computational algorithms
for the estimation of parameters and the calculation of prediction regions.
License: GPL (>= 2)
Encoding: latin1
Repository: CRAN
Depends: R (>= 2.15.1),mirt,gmodels,MASS
LazyData: yes
Archs: i386, x64
NeedsCompilation: no
Packaged: 2014-05-01 20:17:23 UTC; Julio C�sar
Date/Publication: 2014-05-02 07:13:20