Classifies the type of cancer using routinely collected data
    commonly found in cancer registries from pathology reports. The
    package implements the International Classification of Diseases for
    Oncology, 3rd Edition site (topography), histology (morphology), and behaviour codes of neoplasms to classify cancer type 
    <https://www.who.int/standards/classifications/other-classifications/international-classification-of-diseases-for-oncology>. 
    Classification in children utilize the International Classification of Childhood Cancer by Steliarova-Foucher et al. (2005) <doi:10.1002/cncr.20910>.
    Adolescent and young adult cancer classification is based on Barr et al. (2020) <doi:10.1002/cncr.33041>.

Cancer RADAR is a project which aim is to develop an
    infrastructure that allows quantifying the risk of cancer by migration
    background across Europe.  This package contains a set of functions
    cancer registries partners should use to reshape 5 year-age group
    cancer incidence data into a set of summary statistics (see Boyle &
    Parkin (1991, ISBN:978-92-832-1195-2)) in lines with Cancer RADAR data
    protections rules.

Retrieve cancer screening data for cervical, breast and colorectal
    cancers from the Kenya Health Information System <https://hiskenya.org> in a 
    consistent way.

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 higher-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. Methods for
	linear discriminant analysis are now included.