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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.

Type Size Name Uploaded Downloads Labels
conda 248.1 kB | linux-64/r-realvams-0.4_5-r43h884c59f_0.tar.bz2  1 year and 1 month ago 22 main
conda 250.7 kB | linux-64/r-realvams-0.4_3-r42h884c59f_0.tar.bz2  2 years and 7 months ago 51 main
conda 259.2 kB | win-64/r-realvams-0.4_3-r36h796a38f_0.tar.bz2  4 years and 11 months ago 79 main
conda 247.3 kB | osx-64/r-realvams-0.4_3-r36h466af19_0.tar.bz2  4 years and 11 months ago 14 main
conda 249.1 kB | linux-64/r-realvams-0.4_3-r36h29659fb_0.tar.bz2  4 years and 11 months ago 52 main

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