Package: RealVAMS 0.4-6

Andrew Karl

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.

Authors:Andrew Karl [cre, aut], Jennifer Broatch [aut], Jennifer Green [aut]

RealVAMS_0.4-6.tar.gz
RealVAMS_0.4-6.tar.gz(r-4.5-noble)RealVAMS_0.4-6.tar.gz(r-4.4-noble)
RealVAMS_0.4-6.tgz(r-4.4-emscripten)RealVAMS_0.4-6.tgz(r-4.3-emscripten)
RealVAMS.pdf |RealVAMS.html
RealVAMS/json (API)
NEWS

# Install 'RealVAMS' in R:
install.packages('RealVAMS', repos = 'https://cloud.r-project.org')
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

1.00 score 173 downloads 1 exports 5 dependencies

Last updated 12 months agofrom:2bc4b0db26. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKApr 01 2025
R-4.5-linux-x86_64OKApr 01 2025
R-4.4-linux-x86_64OKApr 01 2025

Exports:RealVAMS

Dependencies:latticeMatrixnumDerivRcppRcppArmadillo

Citation

Jennifer Broatch, Jennifer Green, Andrew Karl (2018). RealVAMS: An R Package for Fitting a Multivariate Value-added Model (VAM). The R Journal, 10(1), 22-30. URL https://doi.org/10.32614/RJ-2018-033.

Corresponding BibTeX entry:

  @Article{,
    title = {{RealVAMS: An R Package for Fitting a Multivariate
      Value-added Model (VAM)}},
    author = {Jennifer Broatch and Jennifer Green and Andrew Karl},
    journal = {{The R Journal}},
    year = {2018},
    volume = {10},
    number = {1},
    pages = {22--30},
    doi = {10.32614/RJ-2018-033},
    url = {https://doi.org/10.32614/RJ-2018-033},
  }

Readme and manuals

Help Manual

Help pageTopics
Multivariate VAM FittingRealVAMS-package
Simulated Dataexample.outcome.data
Simulated Dataexample.score.data
Plot method for RealVAMSplot.RealVAMS
Printprint.RealVAMS print.summary.RealVAMS
Internal functionR_mstep2
Multivariate VAM FittingRealVAMS
Internal functionREML_Rm
Summarysummary.RealVAMS
Internal functionvp_cp