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: 2026-10-01 11:04:53 UTC; root
Author: Neelesh Kumar [aut, cre], Atanu Bhattacharjee [aut], Gajendra
        K. Vishwakarma [aut], Tanmoy Majumdar [aut]
Maintainer: Neelesh Kumar <neelesh2302@gmail.com>
Repository: https://neelesh2302-creator.r-universe.dev
Date/Publication: 2026-09-30 13:54:55 UTC
RemoteUrl: https://github.com/cran/MIML
RemoteRef: HEAD
RemoteSha: 9eb513f496b9081139542bf69d98ffdacb4b5c8f