Package: GPvam 3.1-0
GPvam: Maximum Likelihood Estimation of Multiple Membership Mixed Models Used in Value-Added Modeling
An EM algorithm, Karl et al. (2013) <doi:10.1016/j.csda.2012.10.004>, is used to estimate the generalized, variable, and complete persistence models, Mariano et al. (2010) <doi:10.3102/1076998609346967>. These are multiple-membership linear mixed models with teachers modeled as "G-side" effects and students modeled with either "G-side" or "R-side" effects.
Authors:
GPvam_3.1-0.tar.gz
GPvam_3.1-0.zip(r-4.5)GPvam_3.1-0.zip(r-4.4)GPvam_3.1-0.zip(r-4.3)
GPvam_3.1-0.tgz(r-4.4-x86_64)GPvam_3.1-0.tgz(r-4.4-arm64)GPvam_3.1-0.tgz(r-4.3-x86_64)GPvam_3.1-0.tgz(r-4.3-arm64)
GPvam_3.1-0.tar.gz(r-4.5-noble)GPvam_3.1-0.tar.gz(r-4.4-noble)
GPvam_3.1-0.tgz(r-4.4-emscripten)GPvam_3.1-0.tgz(r-4.3-emscripten)
GPvam.pdf |GPvam.html✨
GPvam/json (API)
NEWS
| # Install 'GPvam' in R: |
| install.packages('GPvam', repos = c('https://akarl46556.r-universe.dev', 'https://cloud.r-project.org')) |
- GPvam.benchmark - Benchmarks of the program using simulated data.
- vam_data - Simulated Data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 6 months agofrom:4905ab799a. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 03 2024 |
| R-4.5-win-x86_64 | OK | Sep 03 2024 |
| R-4.5-linux-x86_64 | OK | Sep 03 2024 |
| R-4.4-win-x86_64 | OK | Sep 03 2024 |
| R-4.4-mac-x86_64 | OK | Sep 03 2024 |
| R-4.4-mac-aarch64 | OK | Sep 03 2024 |
| R-4.3-win-x86_64 | OK | Sep 03 2024 |
| R-4.3-mac-x86_64 | OK | Sep 03 2024 |
| R-4.3-mac-aarch64 | OK | Sep 03 2024 |
Exports:GPvamplot.GPvamprint.GPvamsummary.GPvam
Dependencies:latticeMatrixnumDerivRcppRcppArmadillo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Maximum Likelihood Estimation of Multiple Membership Mixed Models Used in Value-Added Modeling | GPvam-package |
| Internal G-side effects function | GP.csh |
| Internal R-side effects function | GP.un |
| Fitting the Generalized and Variable Persistence Models | GPvam |
| Benchmarks of the program using simulated data. | GPvam.benchmark |
| Plot method for GPvam | plot.GPvam |
| print.GPvam print.summary.GPvam | |
| Internal R-side effects function for reduced GP model | rGP.un |
| Summary | summary.GPvam |
| Simulated Data | vam_data |
| Internal R-side effects function for the variable persistence model. | VP.CP.ZP.un |
