Parallel Analysis for Ordinal and Numeric Data using Polychoric
and Pearson Correlations with S3 Classes
Carlos A. Arias <caariasr22@gmail.com> and Victor H. Cervantes
<Herulor@gmail.com>.
R (>= 3.3.0), polycor, ltm, stats, ggplot2, mc2d, sfsmisc
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. This is a continued work
based on a previous version developed at the Colombian Institute
for the evaluation of education - ICFES.

R (>= 3.3.1), polycor, ltm, stats, ggplot2, mc2d, sfsmisc

Peter Filzmoser
Heinrich Fritz
Klaudius Kalcher

Peter Filzmoser Heinrich Fritz Klaudius Kalcher

Peter Filzmoser, Heinrich Fritz, Klaudius Kalcher

Provides functions for robust PCA by projection pursuit.

Provides functions for robust PCA by projection pursuit. The methods are described in Croux et al. (2006) <doi:10.2139/ssrn.968376>, Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>.
Orphaned and corrected on 2018-01-04 as C++ errors were
not corrected.

Provides functions for robust PCA by projection pursuit.
The methods are described in Croux et al. (2006) <doi:10.2139/ssrn.968376>,
Croux et al. (2013) <doi:10.1080/00401706.2012.727746>,
Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>.

Principal Component Analysis for Outlier Detection
Methods to detect genetic markers involved in biological adaptation. 'pcadapt' provides statistical tools for outlier detection based on Principal Component Analysis.