DESCRIPTION

Package: scLANE
Type: Package
Title: Model Gene Expression Dynamics with Spline-Based NB GLMs, GEEs, & GLMMs
Version: 0.99.0
Authors@R: c(person(given = c("Jack", "R."), family = "Leary", email = "j.leary@ufl.edu", role = c("aut", "cre"), comment = c(ORCID = "0009-0004-8821-3269")), 
             person(given = "Rhonda", family = "Bacher", email = "rbacher@ufl.edu", role = c("ctb", "fnd"), comment = c(ORCID = "0000-0001-5787-476X")))
Description: Our scLANE model uses truncated power basis spline models to build flexible, interpretable models of single cell gene expression over pseudotime or latent time. 
             The modeling architectures currently supported are negative-binomial GLMs, GEEs, & GLMMs. 
             Downstream analysis functionalities include model comparison, dynamic gene clustering, smoothed counts generation, gene set enrichment testing, & visualization. 
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.2
Depends: 
  glm2, 
  magrittr, 
  R (>= 3.5.0)
Imports: 
    geeM,
    MASS,
    mpath, 
    dplyr,
    stats,
    utils, 
    withr, 
    purrr,
    tidyr,
    furrr, 
    doSNOW, 
    gamlss,
    scales,
    future, 
    Matrix,
    ggplot2,
    splines, 
    foreach,
    glmmTMB, 
    parallel,
    RcppEigen, 
    bigstatsr,
    tidyselect,
    broom.mixed, 
    Rcpp (>= 1.0.7)
URL: https://github.com/jr-leary7/scLANE
BugReports: https://github.com/jr-leary7/scLANE/issues
Suggests: 
    covr, 
    grid, 
    coop,
    uwot, 
    ggh4x, 
    knitr,
    UCell, 
    irlba, 
    rlang, 
    igraph, 
    gtable, 
    ggpubr, 
    Seurat, 
    bluster, 
    cluster, 
    speedglm, 
    rmarkdown,
    gridExtra, 
    BiocStyle, 
    slingshot, 
    gprofiler2,
    BiocParallel, 
    BiocGenerics, 
    BiocNeighbors, 
    testthat (>= 3.0.0),
    SingleCellExperiment, 
    SummarizedExperiment
VignetteBuilder: knitr
Config/testthat/edition: 3
LinkingTo: 
    Rcpp, 
    RcppEigen
biocViews:
    RNASeq,
    Software,
    Clustering, 
    TimeCourse,
    Sequencing,
    Regression,
    SingleCell, 
    Visualization, 
    GeneExpression,
    Transcriptomics,
    GeneSetEnrichment,
    DifferentialExpression