T4cluster
0.1.4Tools for Cluster Analysis
Overview
Cluster analysis is one of the most fundamental problems in data science. We provide a variety of algorithms from clustering to the learning on the space of partitions. See Hennig, Meila, and Rocci (2016, ISBN:9781466551886) for general exposition to cluster analysis.
Install
Health
- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-04-2512 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-04-2212 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-1811 OK · 2 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
Show 1 earlier snapshots
- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 51%
- Documented parameters
- 97%
- Return-value docs
- 95%
- References docs
- 62%
Downloads
Repository
Stars over time
Forks over time
PRs over time
Repository practices
3 development-tooling and community-health practices detected across 3 families in the upstream repository
Checks run against github.com/kisungyou/t4cluster on 2026-07-19.
Dependencies
Code & Tests
- Cyclomatic complexity
- 1.0 median / 27 max
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
308 40 exported
Complexity
2.3 avg / 27 max
Call network
308 nodes / 203 edges
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
People & History
4 releases. Pick two to compare their code metrics. R releases are shown for context.
Package metadata
- First published
- 2020-09-23
- Total releases
- 4 / 6 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 1.4 KB / 1 file
- Download size
- 273 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet