https://neelesh2302-creator.r-universe.devRecent package updates in neelesh2302-creatorR-universehttps://github.com/neelesh2302-creator.pnghttps://neelesh2302-creator.r-universe.devWed, 30 Sep 2026 13:54:55 GMTneelesh2302@gmail.com (Neelesh Kumar)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.https://github.com/r-universe/neelesh2302-creator/actions/runs/36852757930Wed, 30 Sep 2026 13:54:55 GMTMIML0.1.0successhttps://neelesh2302-creator.r-universe.devhttps://github.com/cran/MIML