Provides functions to analyze the spatial distribution of
    biodiversity, in particular categorical analysis of neo- and paleo-endemism
    (CANAPE) as described in Mishler et al (2014) <doi:10.1038/ncomms5473>.
    'canaper' conducts statistical tests to determine the types of endemism that
    occur in a study area while accounting for the evolutionary relationships of
    species.

Integrated, convenient, and uniform access to Canadian
    Census data and geography retrieved using the 'CensusMapper' API. This package produces analysis-ready 
    tidy data frames and spatial data in multiple formats, as well as convenience functions
    for working with Census variables, variable hierarchies, and region selection. API
    keys are freely available with free registration at <https://censusmapper.ca/api>.
    Census data and boundary geometries are reproduced and distributed on an "as
    is" basis with the permission of Statistics Canada (Statistics Canada 1996; 2001; 2006;
    2011; 2016; 2021).

Generates tree plots with precise branch lengths, gene annotations, and cellular prevalence. The package handles complex tree structures (angles, lengths, etc.) and can be further refined as needed by the user.

Functions to perform the following analyses: i) inferring epistasis from RNAi double knockdown data; ii) identifying gene pairs of multiple mutation patterns; iii) assessing association between gene pairs and survival; and iv) calculating the smallworldness of a graph (e.g., a gene interaction network).  Data and analyses are described in Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. <doi:10.1038/ncomms5828>.

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>.