{ "_id": "6a0fed0facfb0bcc41c73ced", "Package": "ML2Pvae", "Type": "Package", "Title": "Variational Autoencoder Models for IRT Parameter Estimation", "Version": "1.0.0.1", "Authors@R": "c(\nperson(\"Geoffrey\", \"Converse\", email = \"converseg@gmail.com\", role = c(\"aut\", \"cre\", \"cph\")),\nperson(\"Suely\", \"Oliveira\", email = \"suely-oliveira@uiowa.edu\", role = c(\"ctb\", \"ths\")),\nperson(\"Mariana\", \"Curi\", email = \"mcuri@icmc.usp.br\", role = c(\"ctb\"))\n)", "Maintainer": "Geoffrey Converse ", "Description": "Based on the work of Curi, Converse, Hajewski, and\nOliveira (2019) . This package\nprovides easy-to-use functions which create a variational\nautoencoder (VAE) to be used for parameter estimation in Item\nResponse Theory (IRT) - namely the Multidimensional Logistic\n2-Parameter (ML2P) model. To use a neural network as such,\nnontrivial modifications to the architecture must be made, such\nas restricting the nonzero weights in the decoder according to\nsome binary matrix Q. The functions in this package allow for\nstraight-forward construction, training, and evaluation so that\nminimal knowledge of 'tensorflow' or 'keras' is required.", "Note": "The developer version of 'keras' should be used, rather than the\nCRAN version. The latter will cause tests to fail on an initial\nrun, but work on subsequent tries. To avoid this, use\ndevtools::install_github(\"rstudio/keras\"). 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