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RealVAMS
View on CRAN: Click
here
Download and install RealVAMS package within the R console
Install from CRAN:
install.packages("RealVAMS")
Install from Github:
library("remotes")
install_github("cran/RealVAMS")
Install by package version:
library("remotes")
install_version("RealVAMS", "0.4-6")
Attach the package and use:
library("RealVAMS")
Maintained by
Andrew Karl
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2014-11-01
Latest Update: 2023-01-07
Description:
Fits a multivariate value-added model (VAM), see Broatch, Green, and Karl (2018) and Broatch and Lohr (2012) , with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) , 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) . This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.
How to cite:
Andrew Karl (2014). RealVAMS: Multivariate VAM Fitting. R package version 0.4-6, https://cran.r-project.org/web/packages/RealVAMS. Accessed 28 Dec. 2024.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for RealVAMS
Functions, R codes and Examples using
the RealVAMS R package
Some associated functions: REML_Rm . R_mstep2 . RealVAMS-package . RealVAMS . example.outcome.data . example.score.data . plot . print . summary . vp_cp .
Some associated R codes: REML_Rm.R . R_mstep2.R . RealVAMS.R . plot.RealVAMS.R . print.RealVAMS.R . print.summary.RealVAMS.R . summary.RealVAMS.R . vp_cp.R . Full RealVAMS package functions and examples
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