Predicts anticancer peptides using random forests trained on the
    n-gram encoded peptides. The implemented algorithm can be accessed from
    both the command line and shiny-based GUI. The CancerGram model is too large 
    for CRAN and it has to be downloaded separately from the repository:
    <https://github.com/BioGenies/CancerGramModel>. For more information see: 
    Burdukiewicz et al. (2020) <doi:10.3390/pharmaceutics12111045>.

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.

Non-linear/linear hybrid method for batch-effect correction
    that uses Mutual Nearest Neighbors (MNNs) to identify similar cells between 
    datasets. Reference: Loza M. et al. (NAR Genomics and Bioinformatics, 2020) <doi:10.1093/nargab/lqac022>.

A statistical framework and computational procedure for identifying
  the sub-populations within a tumor, determining the mutation profiles of each 
  subpopulation, and inferring the tumor's phylogenetic history. The input are 
  variant allele frequencies (VAFs) of somatic single nucleotide alterations 
  (SNAs) along with allele-specific coverage ratios between the tumor and matched
  normal sample for somatic copy number alterations (CNAs). These quantities can
  be directly taken from the output of existing software. Canopy provides a 
  general mathematical framework for pooling data across samples and sites to 
  infer the underlying parameters. For SNAs that fall within CNA regions, Canopy
  infers their temporal ordering and resolves their phase.  When there are 
  multiple evolutionary configurations consistent with the data, Canopy outputs 
  all configurations along with their confidence assessment.