Machine Learning of Hyperrectangular Gating Strategies for High-Dimensional Cytometry
Overview
Given a high-dimensional dataset that typically represents a cytometry dataset, and a subset of the datapoints, this algorithm outputs an hyperrectangle so that datapoints within the hyperrectangle best correspond to the specified subset. In essence, this allows the conversion of clustering algorithms' outputs to gating strategies outputs.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-03-10
Documentation
- Help pages
- 25
- Examples that run
- 85%11 run in a plain check (1 of them under a condition) · 2 hold no code
- Exported functions with examples
- 100%
- Documented parameters
- 98%
- Return-value docs
- 38%
- Help pages citing references
- 4%
- Hypergate HTML
Downloads
Dependencies
Installing hypergate adds no packages beyond those that come with R.
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 0.8.5Latest
- 0.8.42023-08-31 · diff ↗
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- 0.8.32020-02-06 · diff ↗
- RR 3.6.0 released · 2019-04-26
- 0.8.12019-04-17 · diff ↗
- 0.8
- 0.72018-05-14
- RR 3.5.0 released · 2018-04-23
Package metadata
- First published
- 2018-05-14
- Total releases
- 6 / 8 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 378 KB / 1 file
- Download size
- 526 KB
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