car, heplots (>= 0.8-6), graphics, stats

R (>= 2.10), car, heplots (>= 0.8.6), graphics, stats
c(person(given = "Michael", family = "Friendly",
role=c("aut", "cre"), email="friendly@yorku.ca"), person(given
= "John", family = "Fox", role="aut"))

Visualizing Generalized Canonical Discriminant and Canonical
Correlation Analysis
This package includes functions for computing and
visualizing generalized canonical discriminant analyses for a
multivariate linear model.  Traditional canonical discriminant
analysis is restricted to a one-way MANOVA design and is
equivalent to canonical correlation analysis between a set of
quantitative response variables and a set of dummy variables
coded from the factor variable.  The candisc package
generalizes this to multi-way MANOVA designs for all factors in
a multivariate linear model, computing canonical scores and
vectors for each term. The graphic functions provide low-rank
(1D, 2D, 3D) visualizations of terms in an mlm via the
plot.candisc and heplot.candisc methods.

This package includes functions for computing and
visualizing generalized canonical discriminant analyses and
canonical correlation analysis for a multivariate linear model.
Traditional canonical discriminant analysis is restricted to a
one-way MANOVA design and is equivalent to canonical
correlation analysis between a set of quantitative response
variables and a set of dummy variables coded from the factor
variable.  The candisc package generalizes this to multi-way
MANOVA designs for all factors in a multivariate linear model,
computing canonical scores and vectors for each term. The
graphic functions provide low-rank (1D, 2D, 3D) visualizations
of terms in an mlm via the plot.candisc and heplot.candisc
methods.

This package includes functions for computing and
visualizing generalized canonical discriminant analyses and
canonical correlation analysis for a multivariate linear model.
Traditional canonical discriminant analysis is restricted to a
one-way MANOVA design and is equivalent to canonical
correlation analysis between a set of quantitative response
variables and a set of dummy variables coded from the factor
variable.  The candisc package generalizes this to multi-way
MANOVA designs for all factors in a multivariate linear model,
computing canonical scores and vectors for each term. The
graphic functions provide low-rank (1D, 2D, 3D) visualizations
of terms in an mlm via the plot.candisc and heplot.candisc
methods. Related plots are now provided for canonical
correlation analysis when all predictors are quantitative.