marr
Bioc currentMaximum rank reproducibility
Release Lineage
Entered 3.12 · Oct 28, 2020
Current · Requires R 4.6
Description
marr (Maximum Rank Reproducibility) is a nonparametric approach that detects reproducible signals using a maximal rank statistic for high-dimensional biological data. In this R package, we implement functions that measures the reproducibility of features per sample pair and sample pairs per feature in high-dimensional biological replicate experiments. The user-friendly plot functions in this package also plot histograms of the reproducibility of features per sample pair and sample pairs per feature. Furthermore, our approach also allows the users to select optimal filtering threshold values for the identification of reproducible features and sample pairs based on output visualization checks (histograms). This package also provides the subset of data filtered by reproducible features and/or sample pairs.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
37 5 exported
Complexity
2.5 avg / 8 max
Call network
37 nodes / 25 edges
Test coverage is not measured for Bioconductor packages; nodes fall back to a neutral fill.
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
Code
Structure
Lines of code
2,665
Files
44
Compiled share
10%
Has compiled src
Yes
Language breakdown
API
Exported functions
5
Internal functions
13
Testing & CI
Has tests
Yes
Test-to-code ratio
0.04
testthat edition
–
CI present
Yes
CI type
["travis"]
PR gated
No
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
–
Unsafe pattern score
0
Dep constraint coverage
0%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
4.0
System requirements
–
C++ standard
–
License
GPL (>= 3)
License flags
SPDX valid, OSI approved
History
Versions
12
First release
2021-04-27
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
–
Dep drift
0
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 11%
Topics
People
- Tusharkanti Ghosh author maintainer
- Debashis Ghosh author cph
- Katerina Kechris author
- Max McGrath author
- Daisy Philtron author