Package: immApex
Title: Tools for Adaptive Immune Receptor Sequence-Based Machine and
        Deep Learning
Version: 1.7.1
Authors@R: c(
    person(given = "Nick", family = "Borcherding", role = c("aut", "cre", "cph"), email = "ncborch@gmail.com"),
	person(given = "Qile", family = "Yang", role = "ctb", email = "qile.yang@berkeley.edu", comment = c(ORCID = "0009-0005-0148-2499")))
Description: A set of tools to for machine and deep learning in R from amino acid and nucleotide sequences focusing on adaptive immune receptors. The package includes pre-processing of sequences, unifying gene nomenclature usage, encoding sequences, and combining models. This package will serve as the basis of future immune receptor sequence functions/packages/models compatible with the scRepertoire ecosystem.
License: MIT + file LICENSE
Encoding: UTF-8
biocViews: Software, ImmunoOncology, SingleCell, Classification,
        Annotation, Sequencing, MotifAnnotation
Depends: R (>= 4.3.0)
Imports: immReferent, Matrix, matrixStats, methods, Rcpp,
        SingleCellExperiment, stats, stringr, utils
Suggests: BiocStyle, dplyr, ggraph, ggplot2, igraph, knitr, markdown,
        Peptides, randomForest, rmarkdown, scRepertoire, spelling,
        testthat, tidygraph, viridis
LinkingTo: Rcpp
VignetteBuilder: knitr
Language: en-US
URL: https://github.com/BorchLab/immApex/
BugReports: https://github.com/BorchLab/immApex/issues
Config/roxygen2/version: 8.0.0
git_url: https://git.bioconductor.org/packages/immApex
git_branch: devel
git_last_commit: e9c1c9b
git_last_commit_date: 2026-06-27
Repository: Bioconductor 3.24
Date/Publication: 2026-06-28
NeedsCompilation: yes
Packaged: 2026-06-28 20:44:16 UTC; biocbuild
Author: Nick Borcherding [aut, cre, cph],
  Qile Yang [ctb] (ORCID: <https://orcid.org/0009-0005-0148-2499>)
Maintainer: Nick Borcherding <ncborch@gmail.com>
Built: R 4.6.0; aarch64-apple-darwin23; 2026-06-29 03:45:48 UTC; unix
