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GPvam  

Maximum Likelihood Estimation of Multiple Membership Mixed Models Used in Value-Added Modeling
View on CRAN: Click here


Download and install GPvam package within the R console
Install from CRAN:
install.packages("GPvam")

Install from Github:
library("remotes")
install_github("cran/GPvam")

Install by package version:
library("remotes")
install_version("GPvam", "3.2-0")



Attach the package and use:
library("GPvam")
Maintained by
Andrew Karl
[Scholar Profile | Author Map]
First Published: 2012-02-19
Latest Update: 2023-01-07
Description:
An EM algorithm, Karl et al. (2013) , is used to estimate the generalized, variable, and complete persistence models, Mariano et al. (2010) . These are multiple-membership linear mixed models with teachers modeled as "G-side" effects and students modeled with either "G-side" or "R-side" effects.
How to cite:
Andrew Karl (2012). GPvam: Maximum Likelihood Estimation of Multiple Membership Mixed Models Used in Value-Added Modeling. R package version 3.2-0, https://cran.r-project.org/web/packages/GPvam. Accessed 09 May. 2025.
Previous versions and publish date:
1.0-0 (2012-02-19 22:43), 1.1-0 (2012-04-04 08:21), 2.0-0 (2012-07-06 10:54), 3.0-0 (2014-01-31 08:43), 3.0-1 (2014-01-31 12:14), 3.0-2 (2014-06-23 00:02), 3.0-3 (2015-07-20 09:59), 3.0-4 (2017-03-15 05:25), 3.0-5 (2018-04-19 00:37), 3.0-7 (2021-01-07 03:40), 3.0-8 (2022-09-04 00:00), 3.0-9 (2023-01-08 00:50), 3.1-0 (2024-04-05 10:23), 3.1-1 (2024-10-15 20:20), 3.1-2 (2024-11-18 05:50)
Other packages that cited GPvam R package
View GPvam citation profile
Other R packages that GPvam depends, imports, suggests or enhances
Complete documentation for GPvam
Functions, R codes and Examples using the GPvam R package
Some associated functions: GP.csh . GP.un . GPvam-package . GPvam.benchmark . GPvam . VP.CP.ZP.un . plot . print . rGP.un . summary . vam_data . 
Some associated R codes: GP.csh.R . GP.un.R . GPvam.R . REML_Rm.R . R_mstep2.R . plot.GPvam.R . print.GPvam.R . print.summary.GPvam.R . rGP.un.R . summary.GPvam.R .  Full GPvam package functions and examples
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