Credible Visualization for Two-Dimensional Projections of Data
Overview
Projections are common dimensionality reduction methods, which represent high-dimensional data in a two-dimensional space. However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018] <DOI: 10.1007/978-3-658-20540-9>. This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is derived from the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) DOI:10.1007/978-3-658-20540-9 and the main algorithm called simplified self-organizing map for dimensionality reduction methods is published in <DOI: 10.1016/j.mex.2020.101093>.
Install
Health
NOTERd contentsr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Rd files without \usage: ‘CalcUstarmatrix.Rd’ ‘EsomNeuronsAsList.Rd’ ‘GeneratePmatrix.Rd’ ‘ListAsEsomNeurons.Rd’ ‘XYcoords2LinesColumns.Rd’ ‘setdiffMatrix.Rd’ \arguments should not be documented without \usage.
- NOTE11 OK, 2 NOTENow
- NOTE12 OK, 1 NOTE2026-07-11
- OK12 OK2026-04-25
- NOTE11 OK, 3 NOTE2026-04-22
- ERROR10 OK, 3 NOTE, 1 ERROR2026-04-18
- NOTE11 OK, 3 NOTE2026-03-10
Documentation
- Help pages
- 25includes a package overview page
- Examples that run
- 88%7 run in a plain check · 1 hold no code
- Exported functions with examples
- 40%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 64%
Downloads
Repository
- PRs merged (all time)
- 67%
- Median days to close, 50 most recently updated closed issues
- not measured
- Median days to close, 50 most recently updated closed PRs
- 0
Stars over time
Forks over time
PRs over time
2 development-tooling and community-health practices detected across 2 families in the upstream repository
Checks run against github.com/mthrun/generalizedumatrix on 2026-10-18.
Dependencies
Installing GeneralizedUmatrix adds 20 packages (10 with compiled code, 17 MB of source tarballs). Heaviest: ggplot2 6.0 MB, Rcpp 1.8 MB, isoband 1.5 MB.
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
- 1.3.1Latest
- RR 4.4.0 released · 2024-04-24
- 1.2.6
- RR 4.3.0 released · 2023-04-21
- 1.2.52022-11-28 · diff ↗
- 1.2.42022-05-25 · diff ↗ ·
added 4 exports
UniqueBestMatchingUnits, LowLand, ReduceToLowLand, Uheights4Data - RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 1.2.22021-01-12 · diff ↗
- 1.2.1
- 1.2.02020-08-24 · diff ↗
- RR 4.0.0 released · 2020-04-24
- 1.1.92020-03-23 · diff ↗
- 1.1.8
Show 10 earlier eventsHide 10 earlier events
- 1.1.6
- RR 3.6.0 released · 2019-04-26
- 1.1.52019-02-03 · diff ↗
- 1.1.4
- 1.1.2
- RR 3.5.0 released · 2018-04-23
- 1.0.0
- 0.9.5
- 0.9.42017-05-18
- RR 3.4.0 released · 2017-04-21
Package metadata
- First published
- 2017-05-18
- Total releases
- 16 / 9 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.0
- Bundled data
- 13 KB / 3 files
- Download size
- 724 KB
Cite
Cite this package
Run in R for the authors' entry:
citation("GeneralizedUmatrix")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1016/j.mex.2020.101093.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-22, which the citation names so these numbers can be found later. More on citing and the projects behind them.