kdml
1.1.1Kernel Distance Metric Learning for Mixed-Type Data
Overview
Distance metrics for mixed-type data consisting of continuous, nominal, and ordinal variables. This methodology uses additive and product kernels to calculate similarity functions and metrics, and selects variables relevant to the underlying distance through bandwidth selection via maximum similarity cross-validation. These methods can be used in any distance-based algorithm, such as distance-based clustering. For further details, we refer the reader to Ghashti and Thompson (2024) doi:10.1007/s00357-024-09493-z for dkps() methodology, and Ghashti (2024) doi:10.14288/1.0443975 for dkss() methodology.
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
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 29%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 88%
Downloads
Dependencies
Code & Tests
- Cyclomatic complexity
- 27.0 median / 35 max
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
7 7 exported
Complexity
26.1 avg / 35 max
Call network
7 nodes / 2 edges
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
People & History
3 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- 1.1.1Latest
- 1.1.02024-09-21 · diff ↗
- 1.0.02024-08-27
- RR 4.4.0 released · 2024-04-24
Package metadata
- First published
- 2024-08-27
- Total releases
- 3 / 2 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.5.0
- Download size
- 26 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet