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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "RealVAMS" in publications use:'
type: software
license: GPL-2.0-only
title: 'RealVAMS: Multivariate VAM Fitting'
version: 0.4-6
doi: 10.32614/RJ-2018-033
identifiers:
- type: doi
  value: 10.32614/CRAN.package.RealVAMS
abstract: Fits a multivariate value-added model (VAM), see Broatch, Green, and Karl
  (2018) <https://doi.org/10.32614/RJ-2018-033> and Broatch and Lohr (2012) <https://doi.org/10.3102/1076998610396900>,
  with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood
  approach, Wolfinger (1993) <https://doi.org/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.
authors:
- family-names: Karl
  given-names: Andrew
  email: akarl@asu.edu
  orcid: https://orcid.org/0000-0002-5933-8706
- family-names: Broatch
  given-names: Jennifer
- family-names: Green
  given-names: Jennifer
preferred-citation:
  type: article
  title: 'RealVAMS: An R Package for Fitting a Multivariate Value-added Model (VAM)'
  authors:
  - family-names: Broatch
    given-names: Jennifer
  - family-names: Green
    given-names: Jennifer
  - family-names: Karl
    given-names: Andrew
    email: akarl@asu.edu
    orcid: https://orcid.org/0000-0002-5933-8706
  journal: The R Journal
  year: '2018'
  volume: '10'
  issue: '1'
  doi: 10.32614/RJ-2018-033
  url: https://doi.org/10.32614/RJ-2018-033
  start: '22'
  end: '30'
repository: https://CRAN.R-project.org/package=RealVAMS
date-released: '2024-04-05'
contact:
- family-names: Karl
  given-names: Andrew
  email: akarl@asu.edu
  orcid: https://orcid.org/0000-0002-5933-8706