DESCRIPTION

Package: Mime1
Type: Package
Title: Machine learning-based integration model with elegant performance
Version: 0.0.0.9000
Author@R: c(
    person("Wei", "Zhang", , "xxx", role = c("cre", "aut")),
    person("Hongwei", "Liu", , "xxx", role = "aut"),
    person("Yihao", "Zhang", , "xxx", role = "aut"),
  )
Description: The Mime package provides a user-friendly solution for constructing 
    machine learning-based integration models from transcriptomic data. With the 
    widespread use of high-throughput sequencing technologies, understanding biology 
    and cancer heterogeneity has been revolutionized. Mime streamlines the process of 
    developing predictive models with high accuracy, leveraging complex datasets to 
    identify critical genes associated with disease progression, patient outcomes, 
    and therapeutic response. It offers four main applications (i) establishing 
    prognosis models using 10 machine learning algorithms, (ii) building binary 
    response models with 7 machine learning algorithms, (iii) conducting core feature 
    selection related to prognosis using 8 machine learning methods, and (iv) visualizing 
    the performance of each model. 
License: MIT + file LICENSE
URL: https://github.com/l-magnificence/Mime
BugReports: https://github.com/l-magnificence/Mime/issues
Depends: 
    R (>= 4.1.0)
Imports:
    ComplexHeatmap,
    viridis,
    kknn,
    Matrix,
    BART,
    ROCit,
    caret,
    compareC,
    data.table,
    doParallel,
    dplyr,
    e1071,
    future,
    gbm,
    ggbreak,
    ggplot2,
    ggpubr,
    ggsci,
    glmnet,
    grid,
    gridExtra,
    magrittr,
    meta,
    miscTools,
    pROC,
    plsRcox,
    randomForestSRC,
    readr,
    recipes,
    stringr,
    superpc,
    survival,
    survivalROC,
    survivalsvm,
    tibble,
    tidyr,
    tidyverse,
    compositions,
    pbapply,
    reshape2,
    UpSetR,
    forestploter,
    aplot,
    e1071,
    Ckmeans.1d.dp,
    scales,
    Hmisc,
    Boruta,
    mixtools,
    ROCR
Suggests:
    immunedeconv,
    IOBR,
    knitr,
    rmarkdown,
    testthat
biocViews:
    GSEABase, 
    GSVA,
    cancerclass,
    mixOmics,
    sparrow,
    sva 
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
Config/testthat/edition: 3
VignetteBuilder: knitr