Description: This package provides a function simplifying presentation of PCA models in a 3D interactive representation using rgl

Title: Create 2D Principal Component Plots with Bootstrapping
Description: Draws a 2D principal component plot using the first 2 principal
components from the original and bootstrapped data to give some sense of

Author: Sapan Jot <sapan.madaan@gmail.com> and Paul Brooks
<jpbrooks@vcu.edu>. The scripts configure and configure.ac and
the file INSTALL are based on those in R package clpAPI
Description: This package contains three implementations of principal
component analysis methods using the L1 norm.  The package
depends on COIN-OR Clp version >= 1.12.0.  The methods
implemented are PCA-L1 (Kwak 2008), L1-PCA (Ke and Kanade 2003,
2005), and L1-PCA* (Brooks, Dula, and Boone 2012).

Description: Three implementations of principal component analysis methods
using the L1 norm.  The package depends on COIN-OR Clp version >=
1.12.0.  The methods implemented are PCA-L1 (Kwak 2008), L1-PCA (Ke and
Kanade 2003, 2005), and L1-PCA* (Brooks, Dula, and Boone 2013).

Author: Sapan Jot <sapan.madaan@gmail.com>, Paul Brooks
and Young Woong Park <ywpark@mail.smu.edu>
Description: Implementations of six methods for principal component analysis
L1-PCAhp (Visentin, Prestwich and Armagan 2016)

Description: Implementations of several methods for principal component analysis
SharpEl1-PCA (Brooks and Dula, submitted).

SystemRequirements: COIN-OR Clp (>= 1.17.4)

SharpEL1-PCA (Brooks and Dula, submitted).

Package: pcaPA
Title: Parallel Analysis for ordinal and numeric data using polychoric
and Pearson correlations with S3 classes.
Version: 1.0
Date: 2012-05-04
Author: Carlos A. Arias <carias@icfes.gov.co> and V�ctor 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: 2012-05-06 01:57:28 UTC; herulor
Repository: CRAN
Date/Publication: 2012-05-08 04:07:04