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  "Title": "An Implementation of Two Modern Education-Based Value-Added\nModels",
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  "Authors@R": "c(\nperson(given = \"Garritt\", family = \"L. Page\", email = \"page@stat.byu.edu\", role = c(\"aut\", \"cre\", \"cph\")),\nperson(given = \"S. McKay\", family = \"Curtis\", email = \"s.mckay.curtis@gmail.com\", role = c(\"ctb\", \"cph\")),\nperson(given = \"Radford M.\", family = \"Neal\", role = c(\"ctb\", \"cph\")))",
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  "Description": "Provides functions that fit two modern education-based\nvalue-added models. One of these models is the quantile\nvalue-added model.  This model permits estimating a school's\nvalue-added based on specific quantiles of the post-test\ndistribution.  Estimating value-added based on quantiles of the\npost-test distribution provides a more complete picture of an\neducation institution's contribution to learning for students\nof all abilities. See Page, G.L.; San Martín, E.; Orellana, J.;\nGonzalez, J. (2017) <doi:10.1111/rssa.12195> for more details.\nThe second model is a temporally dependent value-added model.\nThis model takes into account the temporal dependence that may\nexist in school performance between two cohorts in one of two\nways.  The first is by modeling school random effects with a\nnon-stationary AR(1) process.  The second is by modeling school\neffects based on previous cohort's post-test performance. In\naddition to more efficiently estimating value-added, this model\npermits making statements about the persistence of a schools\neffectiveness. The standard value-added model is also an\noption.",
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