Implementations of several methods for principal component analysis
using the L1 norm.  The package depends on COIN-OR Clp version >=
1.12.0.  The methods implemented are
PCA-L1 (Kwak 2008) <DOI:10.1109/TPAMI.2008.114>,
L1-PCA (Ke and Kanade 2003, 2005) <DOI:10.1109/CVPR.2005.309>,
L1-PCA* (Brooks, Dula, and Boone 2013) <DOI:10.1016/j.csda.2012.11.007>,
L1-PCAhp (Visentin, Prestwich and Armagan 2016)
<DOI:10.1007/978-3-319-46227-1_37>,
wPCA (Park and Klabjan 2016),
awPCA (Park and Klabjan 2016),
PCA-Lp (Kwak 2014) <DOI:10.1109/TCYB.2013.2262936>, and
SharpEL1-PCA (Brooks and Dula, submitted).

Parallel Analysis for ordinal and numeric data using polychoric
and Pearson correlations with S3 classes.
Carlos A. Arias <carias@icfes.gov.co> and Víctor H. Cervantes
<vcervantes@icfes.gov.co>.
R (>= 2.14.1), polycor, ltm, stats, ggplot2, mc2d
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

Carlos A. Arias <carias@icfes.gov.co> and Victor H. Cervantes
<vcervantes@icfes.gov.co>.
R (>= 3.0.0), polycor, ltm, stats, ggplot2, mc2d
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.