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MIML  

Machine Learning Imputation, Clustering and Survival Analysis for Longitudinal Proteomic Data
View on CRAN: Click here


Download and install MIML package within the R console
Install from CRAN:
install.packages("MIML")

Install from Github:
library("remotes")
install_github("cran/MIML")

Install by package version:
library("remotes")
install_version("MIML", "0.1.0")



Attach the package and use:
library("MIML")
Maintained by
Neelesh Kumar
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-09-30
Latest Update: 2026-09-30
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.
How to cite:
Neelesh Kumar (2026). MIML: Machine Learning Imputation, Clustering and Survival Analysis for Longitudinal Proteomic Data. R package version 0.1.0, https://cran.r-project.org/web/packages/MIML. Accessed 24 Oct. 2026.
Previous versions and publish date:
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Complete documentation for MIML
Functions, R codes and Examples using the MIML R package
Full MIML package functions and examples
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