Package: MIML
Type: Package
Title: Machine Learning Imputation, Clustering and Survival Analysis
        for Longitudinal Proteomic Data
Version: 0.1.0
Authors@R: c(
    person("Neelesh", "Kumar", email = "neelesh2302@gmail.com",
           role = c("aut", "cre")),
    person("Atanu", "Bhattacharjee", role = "aut"),
    person(c("Gajendra", "K."), "Vishwakarma", role = "aut"),
    person("Tanmoy", "Majumdar", role = "aut"))
Description: Imputes missing biomarker measurements in a wide longitudinal
    serum panel with gradient-boosted decision trees, groups the completed
    panel by Bayesian consensus clustering, and compares the resulting patient
    subgroups by Kaplan-Meier, log-rank and Cox analysis. The imputation
    learner is described in Ke et al. (2017)
    <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree>
    and the clustering method in Lock and Dunson (2013)
    <doi:10.1093/bioinformatics/btt425>. Imputed values are conditional-mean
    predictions, so the procedure is a machine-learning single imputation; the
    completions carry no between-imputation variance and must not be pooled by
    Rubin's rules. Two panels from Gene Expression Omnibus accession
    'GSE65622' are included, one for each survival endpoint.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: lightgbm (>= 3.3.0), data.table, survival, stats, utils
Suggests: BCClong, testthat (>= 3.0.0)
Config/testthat/edition: 3
LazyData: true
LazyDataCompression: xz
Config/roxygen2/version: 8.1.0
NeedsCompilation: no
Packaged: 2026-09-21 12:50:13 UTC; DELL
Author: Neelesh Kumar [aut, cre],
  Atanu Bhattacharjee [aut],
  Gajendra K. Vishwakarma [aut],
  Tanmoy Majumdar [aut]
Maintainer: Neelesh Kumar <neelesh2302@gmail.com>
Repository: CRAN
Date/Publication: 2026-09-30 10:10:02 UTC
Built: R 4.7.0; ; 2026-09-30 23:53:41 UTC; windows
