Maximum Likelihood Estimation of Multiple Membership Mixed Models Used in Value-Added Modeling

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.


News

Version 3.0-5: 2018-04-18 - Minor updates to package structure to conform with new CRAN requirements.

Version 3.0-4: 2017-03-15 - Minor updates to package structure to conform with new CRAN requirements related to compiled C++ code.

Version 3.0-3: 2015-07-19 - Minor updates to package structure to conform with new CRAN requirements.

Version 3.0-2: 2014-06-22

- Changed method used to calculate rank of X matrix to 
  avoid conversion to a dense matrix.

- Added information to documentation about program run time.

- Added data set GPvam.benchmark, which contains program run time
  and memory requirements for various simulated data sets.

- Changed default options to Hessian=FALSE and verbose=TRUE.

Version 3.0-1: 2014-01-31

- Corrected use of 'break' reserved word

Version 3.0-0: 2014-01-30

- The program runs much faster now with student.side="R"
  due to the use of Rcpp and RcppArmadillo and a re-ordered
  nested loop.

- Added references to recently accepted papers in
      help files.

Version 2.0-0: 2012-07-05

- Improved scalability of program when student.side="R". 
  For smaller data sets, certain operations work faster 
  with dense matrices, but these matrices grow in size 
  with the data. For large data sets, we now rely 
  exclusively on sparse matrices.

- Added support for the variable persistence (VP) model,
  including the ability to estimate the persistence 
  parameters. As special cases, the complete and zero 
  persistence models have also been included.

Version 1.1-0: 2012-04-03

- Corrected issue where program crashed when updated
      covariance matrix was not symmetric due to rounding
      (i.e. transposed compoenents may have differered by
      1E-14 or so).
    
    - Added caterpillar plots to plot() output.

Version 1.0-0: 2012-02-19

    - First public release.

Reference manual

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install.packages("GPvam")

3.0-5 by Andrew Karl, 9 months ago


Browse source code at https://github.com/cran/GPvam


Authors: Andrew Karl , Yan Yang , and Sharon Lohr


Documentation:   PDF Manual  


GPL-2 license


Imports numDeriv, Rcpp, graphics, grDevices, methods, stats, utils

Depends on Matrix

Linking to Rcpp, RcppArmadillo


See at CRAN