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RealVAMS: Multivariate VAM Fitting
Fits a multivariate value-added model (VAM), see Broatch, Green, and Karl (2018) <doi:10.32614/RJ-2018-033> and Broatch and Lohr (2012) <doi:10.3102/1076998610396900>, with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) <doi:10.1080/00949659308811554>, is used for the estimation of this joint generalized linear mixed model. The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) <doi:10.1016/j.csda.2012.10.004>. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.
| Version: |
0.4-6 |
| Depends: |
R (≥ 3.0.0), Matrix |
| Imports: |
numDeriv, Rcpp (≥ 0.11.2), methods, stats, utils, grDevices, graphics |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Published: |
2024-04-05 |
| DOI: |
10.32614/CRAN.package.RealVAMS |
| Author: |
Andrew Karl  [cre,
aut],
Jennifer Broatch [aut],
Jennifer Green [aut] |
| Maintainer: |
Andrew Karl <akarl at asu.edu> |
| License: |
GPL-2 |
| NeedsCompilation: |
yes |
| Citation: |
RealVAMS citation info |
| Materials: |
NEWS |
| CRAN checks: |
RealVAMS results |
Documentation:
Downloads:
Linking:
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https://CRAN.R-project.org/package=RealVAMS
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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