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.

Package details

AuthorAndrew T. Karl, Jennifer Broatch, and Jennifer Green
MaintainerAndrew Karl <[email protected]>
LicenseGPL-2
Version0.4-3
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("RealVAMS")

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RealVAMS documentation built on May 1, 2019, 8:48 p.m.