This package provides new relational operators useful for performing

Title: Spatio-Temporal Fixation Pattern Analysis
Description: Spatio-temporal Fixation Pattern Analysis (FPA) is a new method of analyzing eye
movement data, developed by Mr. Jinlu Cao under the supervision of Prof. Chen Hsuan-Chih at
The Chinese University of Hong Kong, and Prof. Wang Suiping at the South China Normal
Univeristy. The package "fpa" is a R implementation which makes FPA analysis much easier.
There are four major functions in the package: ft2fp(), get_pattern(), plot_pattern(), and
lineplot(). The function ft2fp() is the core function, which can complete all the preprocessing
within moments. The other three functions are supportive functions which visualize the eye

Title: Fixed point clusters, clusterwise regression and discriminant plots
Description: Fuzzy and crisp fixed point cluster analysis
based on Mahalanobis distance and linear regression fixed point
clusters. Semi-explorative, semi-model-based clustering methods,
operating on n*p data, do not need prespecification of number of
clusters, produce overlapping clusters. Discriminant projections separate
groups optimally, used to visualize the separation of
groupings. Corresponding plot methods. Clusterwise linear

clusters, produce overlapping clusters. Symmetric and asymmetric
regression by normal mixture modeling. Cluster validation statistics

Package: fpc
Title: Fixed point clusters, clusterwise regression and discriminant plots
Version: 1.1-2
Date: 2004-04-23
Author: Christian Hennig <hennig@math.uni-hamburg.de>
Depends: R (>= 1.5), MASS, cluster
Description: Fuzzy and crisp fixed point cluster analysis
based on Mahalanobis distance and linear regression fixed point
clusters. Semi-explorative, semi-model-based clustering methods,
operating on n*p data, do not need prespecification of number of
clusters, produce overlapping clusters. Symmetric and asymmetric
discriminant projections separate
groups optimally, used to visualize the separation of
groupings. Corresponding plot methods. Clusterwise linear
regression by normal mixture modeling. Cluster validation statistics
for distance based clustering. Note: If you use an R-version older
than 1.9.0, you will need packages lqs and mva installed.
Maintainer: Christian Hennig <hennig@math.uni-hamburg.de>
License: GPL
URL: http://www.math.uni-hamburg.de/home/hennig/