Qualitative Validation Methods
Overview
Qualitative methods for the validation of dynamic models. It contains (i) an orthogonal set of deviance measures for absolute, relative and ordinal scale and (ii) approaches accounting for time shifts. The first approach transforms time to take time delays and speed differences into account. The second divides the time series into interval units according to their main features and finds the longest common subsequence (LCS) using a dynamic programming algorithm.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 11includes a package overview page
- Examples that run
- 100%9 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 86%
- Return-value docs
- 100%
- Help pages citing references
- 55%
Also includes 1 demo script (run with demo())
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Dependencies
Installing qualV adds no packages beyond those that come with R.
Code & Tests
Datasets
People & History
Previously maintained by K. Gerald van den Boogaart (0.2-3 to 0.2-4).
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Package metadata
- First published
- 2007-05-31
- Total releases
- 10 / 19 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 2.0.0
- Bundled data
- 3.9 KB / 1 file
- Download size
- 29 KB
Cite
Cite this package
Run in R for the authors' entry:
citation("qualV")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.18637/jss.v022.i08.
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For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-21, which the citation names so these numbers can be found later. More on citing and the projects behind them.