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cff-version: 1.2.0
message: 'To cite package "MIML" in publications use:'
type: software
license: GPL-3.0-only
title: 'MIML: Machine Learning Imputation, Clustering and Survival Analysis for Longitudinal
  Proteomic Data'
version: 0.1.0
doi: 10.32614/CRAN.package.MIML
abstract: 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) <https://doi.org/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.
authors:
- family-names: Kumar
  given-names: Neelesh
  email: neelesh2302@gmail.com
- family-names: Bhattacharjee
  given-names: Atanu
- family-names: Vishwakarma
  given-names: Gajendra K.
- family-names: Majumdar
  given-names: Tanmoy
repository: https://neelesh2302-creator.r-universe.dev
commit: 9eb513f496b9081139542bf69d98ffdacb4b5c8f
date-released: '2026-09-30'
contact:
- family-names: Kumar
  given-names: Neelesh
  email: neelesh2302@gmail.com