Package: stuart 0.10.2
stuart: Subtests Using Algorithmic Rummaging Techniques
Construct subtests from a pool of items by using ant-colony-optimization, genetic algorithms, brute force, or random sampling. Schultze (2017) <doi:10.17169/refubium-622>.
Authors:
stuart_0.10.2.tar.gz
stuart_0.10.2.zip(r-4.5)stuart_0.10.2.zip(r-4.4)stuart_0.10.2.zip(r-4.3)
stuart_0.10.2.tgz(r-4.5-any)stuart_0.10.2.tgz(r-4.4-any)stuart_0.10.2.tgz(r-4.3-any)
stuart_0.10.2.tar.gz(r-4.5-noble)stuart_0.10.2.tar.gz(r-4.4-noble)
stuart_0.10.2.tgz(r-4.4-emscripten)stuart_0.10.2.tgz(r-4.3-emscripten)
stuart.pdf |stuart.html✨
stuart/json (API)
| # Install 'stuart' in R: |
| install.packages('stuart', repos = c('https://martscht.r-universe.dev', 'https://cloud.r-project.org')) |
- fairplayer - MTMM fairplayer Intervention Data
- sia - Data from a German Meaning of Work Scale.
- sups - Data from a scale for Supervisor Support
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:d26db14d6c. Checks:4 OK, 5 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 31 2025 |
| R-4.5-win | OK | Mar 31 2025 |
| R-4.5-mac | OK | Mar 31 2025 |
| R-4.5-linux | OK | Mar 31 2025 |
| R-4.4-win | NOTE | Mar 31 2025 |
| R-4.4-mac | NOTE | Mar 31 2025 |
| R-4.4-linux | NOTE | Mar 31 2025 |
| R-4.3-win | NOTE | Mar 31 2025 |
| R-4.3-mac | NOTE | Mar 31 2025 |
Exports:as.stuartFixedObjectivebruteforcecombinationscrossvalidateempiricalobjectivefixedobjectivegeneheuristicsholdoutkfoldmmasobjectivematricesrandomsamples
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| STUART: Subtests Using Algorithmic Rummaging Techniques | stuart-package stuart |
| Convert empirical to fixed objective. | as.stuartFixedObjective |
| Subtest construction using a brute-force approach | bruteforce |
| Compute the number of possible subtest combinations | combinations |
| Cross-Validate a Measurement Model | crossvalidate |
| Generate an empirical objective function for item selection. | empiricalobjective |
| MTMM fairplayer Intervention Data (2009) | fairplayer |
| Generate a fixed objective function for item selection. | fixedobjective |
| Subtest construction using a simple genetic algorithm | gene |
| Generating heuristics for the use in STUART subtest construction | heuristics |
| Data selection for holdout validation. | holdout |
| k-Folds Crossvalidation | kfold |
| Subtest construction using the Max-Min-Ant-System | mmas |
| Generate matrix-components for objective functions. | objectivematrices |
| Generating random samples of Subtests | randomsamples |
| Data from a German Meaning of Work Scale. | sia |
| Data from a scale for Supervisor Support | sups |
