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DICErClust

0.1.3

Deep Significance Clustering for Clinical Risk Stratification

0packages depend
670downloads / year
test coverage
13/13checks pass

Overview

About
Maintained by Sarah AytonFirst published 2026-05-282 releasesCRAN page ↗

We provide an R implementation of Deep Significance Clustering (DICE), a self-supervised learning framework designed to identify clinically meaningful and risk-stratified patient subgroups from electronic health record (EHR) data. DICE jointly optimizes deep representation learning, clustering, and outcome prediction while enforcing statistical significance between predicted outcomes and cluster membership. This integrated optimization produces subgroups that are both clinically coherent and predictive, addressing a gap where traditional unsupervised clustering methods and supervised risk prediction models alone may fail to generate actionable clinical groupings. See Huang et al. (2021) doi:10.1093/jamia/ocab203.

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Health

CRAN checks
13OK
Slowest check: 3.0 min · r-devel-linux-x86_64-fedora-clang
Code health
None
Tests · ratio 0.00
not tracked
Coverage
100%
Documentation · exports
4
Dependencies · direct
Check history
  • OK2026-05-29
    7 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

Documentation
READMEYes · 302 wordsVignettesYes · dynamicpkgdown siteNoNEWSNoCode of conductNoContributing guideNo
Examples that run
21%
Documented parameters
100%
Return-value docs
100%
References docs
0%

Downloads

670
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Dependencies

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7 external dependencies (excludes base and recommended)
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Enhances (0)
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Reverse dependencies
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Code & Tests

People & History

People (2)
Maintainer (1)
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Authors (2)
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Package Timeline

2 releases. Pick two to compare their code metrics. R releases are shown for context.

  • 0.1.3Latest
    2026-07-09 · current release · diff ↗
  • 0.1.2
    2026-05-28
  • R
    R 4.6.0 released · 2026-04-24

Package metadata

First published
2026-05-28
Total releases
2 / 1 yrs
License
MIT + file LICENSE OSI
Download size
not tracked yet
Installed size
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