Package: immApex
Title: Tools for Adaptive Immune Receptor Sequence-Based Machine and
        Deep Learning
Version: 1.5.0
Authors@R: c(
    person(given = "Nick", family = "Borcherding", role = c("aut", "cre"), email = "ncborch@gmail.com"))
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
RoxygenNote: 7.3.2
biocViews: Software, ImmunoOncology, SingleCell, Classification,
        Annotation, Sequencing, MotifAnnotation
Depends: R (>= 4.3.0)
Imports: hash, httr, Matrix, matrixStats, methods, Rcpp, rvest,
        SingleCellExperiment, stats, stringr, utils
Suggests: BiocStyle, dplyr, ggraph, ggplot2, igraph, knitr, markdown,
        Peptides, randomForest, rmarkdown, scRepertoire, spelling,
        testthat, tidygraph, viridis
SystemRequirements: Python (via basilisk)
LinkingTo: Rcpp
VignetteBuilder: knitr
Language: en-US
URL: https://github.com/BorchLab/immApex/
BugReports: https://github.com/BorchLab/immApex/issues
Config/pak/sysreqs: libicu-dev libxml2-dev libssl-dev python3
        zlib1g-dev
Repository: https://bioc.r-universe.dev
Date/Publication: 2025-10-29 15:32:52 UTC
RemoteUrl: https://github.com/bioc/immApex
RemoteRef: HEAD
RemoteSha: 1dd44fc9e35ec3f6d6e029f0162776f43d68b50e
NeedsCompilation: yes
Packaged: 2025-11-09 07:14:28 UTC; root
Author: Nick Borcherding [aut, cre]
Maintainer: Nick Borcherding <ncborch@gmail.com>
Built: R 4.6.0; x86_64-w64-mingw32; 2025-11-09 07:17:31 UTC; windows
Archs: x64
