Multivariate VAM Fitting R package RealVAMS version 0.4-6 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. Software Depends: R (>= 3.0.0), Matrix Imports: numDeriv, Rcpp (>= 0.11.2), methods, stats, utils, grDevices, graphics LinkingTo: Rcpp, RcppArmadillo Andrew Karl <akarl@asu.edu> Comprehensive R Archive Network (CRAN) Andrew Karl [cre, aut] (ORCID: <https://orcid.org/0000-0002-5933-8706>), Jennifer Broatch [aut], Jennifer Green [aut] GPL-2 2024-04-05 application/tgz https://CRAN.R-project.org/package=RealVAMS doi:10.32614/CRAN.package.RealVAMS