Package: Dire 2.2.0
Dire: Linear Regressions with a Latent Outcome Variable
Fit latent variable linear models, estimating score distributions for groups of people, following Cohen and Jiang (1999) <doi:10.2307/2669917>. In this model, a latent distribution is conditional on students item response, item characteristics, and conditioning variables the user includes. This latent trait is then integrated out. This software is intended to fit the same models as the existing software 'AM' <https://am.air.org/>. As of version 2, also allows the user to draw plausible values.
Authors:
Dire_2.2.0.tar.gz
Dire_2.2.0.zip(r-4.5)Dire_2.2.0.zip(r-4.4)Dire_2.2.0.zip(r-4.3)
Dire_2.2.0.tgz(r-4.4-x86_64)Dire_2.2.0.tgz(r-4.4-arm64)Dire_2.2.0.tgz(r-4.3-x86_64)Dire_2.2.0.tgz(r-4.3-arm64)
Dire_2.2.0.tar.gz(r-4.5-noble)Dire_2.2.0.tar.gz(r-4.4-noble)
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Dire.pdf |Dire.html✨
Dire/json (API)
NEWS
# Install 'Dire' in R: |
install.packages('Dire', repos = c('https://american-institutes-for-research.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/american-institutes-for-research/dire/issues
Last updated 1 years agofrom:2dbe7e4dc0. Checks:OK: 1 WARNING: 8. Indexed: yes.
Target | Result | Date |
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Doc / Vignettes | OK | Oct 26 2024 |
R-4.5-win-x86_64 | WARNING | Oct 26 2024 |
R-4.5-linux-x86_64 | WARNING | Oct 26 2024 |
R-4.4-win-x86_64 | WARNING | Oct 26 2024 |
R-4.4-mac-x86_64 | WARNING | Oct 26 2024 |
R-4.4-mac-aarch64 | WARNING | Oct 26 2024 |
R-4.3-win-x86_64 | WARNING | Oct 26 2024 |
R-4.3-mac-x86_64 | WARNING | Oct 26 2024 |
R-4.3-mac-aarch64 | WARNING | Oct 26 2024 |
Dependencies:bitbit64clicliprcodetoolscpp11crayonfansiforcatsforeachgluehavenhmsiteratorslatticelbfgslifecyclemagrittrMASSMatrixpillarpkgconfigprettyunitsprogressR6RcppRcppArmadilloreadrrlangtibbletidyselecttzdbutf8vctrsvroomwithr
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Draw plausible values (PVs) from an mml fit | drawPVs drawPVs.mmlCompositeMeans drawPVs.mmlMeans drawPVs.summary.mmlMeans |
Marginal Maximum Likelihood Estimation of Linear Models | mml |