DESCRIPTION

Package: pyRforest
Title: A comprehensive approach to genomic analysis using
        scikit-learn's Random Forest models and rank-based feature
        reduction in R
Version: 4.3.4
Authors@R: 
    person("Tyler", "Kolisnik", , "tkolisnik@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-2740-4219"))
Description: Welcome to pyRforest, a comprehensive tool for genomic data analysis featuring scikit-learn Random Forests in R. Tailored for expression data, such as RNA-seq or Microarray, pyRforest is built for bioinformaticians and researchers looking to explore the relationship between biological features and matched binary or categorical outcome variables using Random Forest models. Please read on for instructions that will guide you through pyRforest's seamless integration of scikit-learn's Random Forest methodologies (imported to R via reticulate) for model development, evaluation, SHAPley additive explanations, and our custom feature reduction approach by way of rank-based permutation. You will also be directed you through our integration with clusterProfiler and g:Profiler for Gene Ontology and Enrichment Analysis.
License: BSD 3-Clause License + LICENSE
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3
Imports: dplyr, ggplot2, stats, readr, reticulate, gprofiler2,
        ggbeeswarm, cowplot, tidyr
Suggests: rmarkdown, knitr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-08-20 00:06:40 UTC; tyler
Author: Tyler Kolisnik [aut, cre] (<https://orcid.org/0000-0003-2740-4219>)
Maintainer: Tyler Kolisnik <tkolisnik@gmail.com>
Depends: R (>= 3.5.0)