Tests for context-dependent selection on cancer driver genes
    using somatic mutation data. The package implements the DiffDriver
    statistical framework to assess whether the strength of positive selection
    on mutations in a driver gene is associated with tumor- or individual-level
    context variables, such as clinical traits, genomic features, or immune
    microenvironment subtypes. DiffDriver estimates individual- and
    position-specific background mutation rates, models selection as a deviation
    from the background rate using functional annotations, and tests context
    effects through a latent-variable logistic model. It provides utilities for
    preparing mutation and annotation data, fitting differential-selection
    models, running gene-level association tests, summarizing candidate genes,
    and visualizing mutation patterns. The method is described in Zhou et al.
    (2026) "Detecting context-dependent selection on cancer driver genes with
    DiffDriver" <doi:10.64898/2026.04.06.716771>.

Compare functional enrichment between two experimentally-derived groups of genes or proteins (Peterson, DR., et al.(2018)) <doi: 10.1371/journal.pone.0198139>. Given a list of gene symbols, 'diffEnrich'  will
  perform differential enrichment analysis using the Kyoto Encyclopedia of Genes
  and Genomes (KEGG) REST API. This package provides a number of functions that are 
  intended to be used in a pipeline. Briefly, the user provides a KEGG formatted species id for either human, mouse or rat, and the package will
  download and clean species specific ENTREZ gene IDs and map them to their respective
  KEGG pathways by accessing KEGG's REST API. KEGG's API is used to guarantee the most up-to-date pathway data from KEGG. Next, the user will identify significantly
  enriched pathways from two gene sets, and finally, the user will identify 
  pathways that are differentially enriched between the two gene sets. In addition to 
  the analysis pipeline, this package also provides a plotting function.