Package: BTLLasso 0.1-13
BTLLasso: Modelling Heterogeneity in Paired Comparison Data
Performs 'BTLLasso' as described by Schauberger and Tutz (2019) <doi:10.18637/jss.v088.i09> and Schauberger and Tutz (2017) <doi:10.1177/1471082X17693086>. BTLLasso is a method to include different types of variables in paired comparison models and, therefore, to allow for heterogeneity between subjects. Variables can be subject-specific, object-specific and subject-object-specific and can have an influence on the attractiveness/strength of the objects. Suitable L1 penalty terms are used to cluster certain effects and to reduce the complexity of the models.
Authors:
BTLLasso_0.1-13.tar.gz
BTLLasso_0.1-13.zip(r-4.5)BTLLasso_0.1-13.zip(r-4.4)BTLLasso_0.1-13.zip(r-4.3)
BTLLasso_0.1-13.tgz(r-4.4-x86_64)BTLLasso_0.1-13.tgz(r-4.4-arm64)BTLLasso_0.1-13.tgz(r-4.3-x86_64)BTLLasso_0.1-13.tgz(r-4.3-arm64)
BTLLasso_0.1-13.tar.gz(r-4.5-noble)BTLLasso_0.1-13.tar.gz(r-4.4-noble)
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BTLLasso.pdf |BTLLasso.html✨
BTLLasso/json (API)
| # Install 'BTLLasso' in R: |
| install.packages('BTLLasso', repos = c('https://schaubert.r-universe.dev', 'https://cloud.r-project.org')) |
- Buli1415 - Bundesliga Data 2014/15
- Buli1516 - Bundesliga Data 2015/16
- Buli1617 - Bundesliga Data 2016/17
- Buli1718 - Bundesliga Data 2017/18
- BuliResponse - Bundesliga Data Response Data
- GLES - German Longitudinal Election Study
- GLESsmall - Subset of the GLES data set with 200 observations and 4 covariates.
- SimData - Simulated data set for illustration
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 9 months agofrom:0a4810de62. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 09 2024 |
| R-4.5-win-x86_64 | OK | Nov 09 2024 |
| R-4.5-linux-x86_64 | OK | Nov 09 2024 |
| R-4.4-win-x86_64 | OK | Nov 09 2024 |
| R-4.4-mac-x86_64 | OK | Nov 09 2024 |
| R-4.4-mac-aarch64 | OK | Nov 09 2024 |
| R-4.3-win-x86_64 | OK | Nov 09 2024 |
| R-4.3-mac-x86_64 | OK | Nov 09 2024 |
| R-4.3-mac-aarch64 | OK | Nov 09 2024 |
Exports:boot.BTLLassoBTLLassoctrl.BTLLassocv.BTLLassopathsresponse.BTLLasso
Dependencies:cligluelatticelifecyclemagrittrMatrixpsychotoolsRcppRcppArmadillorlangstringistringrvctrs
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| BTLLasso | BTLLasso-package |
| Bootstrap function for BTLLasso | boot.BTLLasso |
| Function to perform BTLLasso | BTLLasso |
| Bundesliga Data 2014/15 (Buli1415) | Buli1415 |
| Bundesliga Data 2015/16 (Buli1516) | Buli1516 |
| Bundesliga Data 2016/17 (Buli1617) | Buli1617 |
| Bundesliga Data 2017/18 (Buli1718) | Buli1718 |
| Bundesliga Data Response Data (BuliResponse) | BuliResponse |
| Control function for BTLLasso | ctrl.BTLLasso |
| Cross-validation function for BTLLasso | cv.BTLLasso |
| German Longitudinal Election Study (GLES) | GLES |
| Subset of the GLES data set with 200 observations and 4 covariates. | GLESsmall |
| Plot covariate paths for BTLLasso | paths |
| Plot bootstrap intervals for BTLLasso | plot.boot.BTLLasso |
| Plot parameter paths for BTLLasso | plot.BTLLasso |
| Predict function for BTLLasso | predict.BTLLasso |
| Print function for boot.BTLLasso objects | print.boot.BTLLasso |
| Print function for BTLLasso objects | print.BTLLasso |
| Print function for cv.BTLLasso objects | print.cv.BTLLasso |
| Create response object for BTLLasso | response.BTLLasso |
| Simulated data set for illustration | SimData |
