Package: pcaPA
Title: Parallel Analysis for ordinal and numeric data using polychoric
and Pearson correlations with S3 classes.
Version: 1.1
Date: 2012-05-04
Author: Carlos A. Arias <carias@icfes.gov.co> and Víctor H. Cervantes
<vcervantes@icfes.gov.co>.
SystemRequirements: Gnu Scientific Library version >= 1.18
Maintainer: Carlos A. Arias <carias@icfes.gov.co>
Depends: R (>= 2.14.1), polycor, ltm, stats, ggplot2, mc2d
Description: A set of functions to perform parallel analysis for
principal components analysis intended mainly for large data
sets. It performs a parallel analysis of continuous, ordered
(including dichotomous/binary as a special case) or mixed type
of data associated with a principal components analysis.
Polychoric correlations among ordered variables, Pearson
correlations among continuous variables and polyserial
correlation between mixed type variables (one ordered and one
continuous) are used. Whenever the use of polyserial or
polychoric correlations yields a non positive definite
correlation matrix, the resulting matrix is transformed into
the nearest positive definite matrix. For details on the GSL
installation in windows please visit the following link:
http://wiki.rglab.org/index.php?title=Public:Installation_of_GSL_from_Windows_binary
License: GPL (>= 2)
Encoding: latin1
Packaged: 2012-08-14 20:39:42 UTC; carias
Repository: CRAN
Date/Publication: 2012-08-15 05:19:54

Package: pcaPA
Title: Parallel Analysis for ordinal and numeric data using polychoric
and Pearson correlations with S3 classes.
Version: 1.2
Date: 2013-23-20
Author: Carlos A. Arias <carias@icfes.gov.co> and Victor H. Cervantes
<vcervantes@icfes.gov.co>.
Maintainer: Carlos A. Arias <carias@icfes.gov.co>
Depends: R (>= 3.0.0), polycor, ltm, stats, ggplot2, mc2d
Description: A set of functions to perform parallel analysis for
principal components analysis intended mainly for large data
sets. It performs a parallel analysis of continuous, ordered
(including dichotomous/binary as a special case) or mixed type
of data associated with a principal components analysis.
Polychoric correlations among ordered variables, Pearson
correlations among continuous variables and polyserial
correlation between mixed type variables (one ordered and one
continuous) are used. Whenever the use of polyserial or
polychoric correlations yields a non positive definite
correlation matrix, the resulting matrix is transformed into
the nearest positive definite matrix.
License: GPL (>= 2)
Packaged: 2013-12-20 17:46:43 UTC; Carlos
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2013-12-21 00:15:37