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 ORCID iD [cre, aut], Jennifer Broatch [aut], Jennifer Green [aut]
Maintainer: Andrew Karl <akarl at>
License: GPL-2
NeedsCompilation: yes
Citation: RealVAMS citation info
Materials: NEWS
CRAN checks: RealVAMS results


Reference manual: RealVAMS.pdf


Package source: RealVAMS_0.4-6.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): RealVAMS_0.4-6.tgz, r-oldrel (arm64): RealVAMS_0.4-6.tgz, r-release (x86_64): RealVAMS_0.4-6.tgz, r-oldrel (x86_64): RealVAMS_0.4-6.tgz
Old sources: RealVAMS archive


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