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  "Title": "Model-Based Response Dimension Reduction",
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  "Author": "Jae Keun Yoo",
  "Maintainer": "Jae Keun Yoo <peter.yoo@ewha.ac.kr>",
  "Description": "Functions for model-based response dimension reduction.\nUsual dimension reduction methods in multivariate regression\nfocus on the reduction of predictors, not responses.  The\nresponse dimension reduction is theoretically founded in Yoo\nand Cook (2008) <doi:10.1016/j.csda.2008.07.029>. Later, three\nmodel-based response dimension reduction approaches are\nproposed in Yoo (2016) <doi:10.1080/02331888.2017.1410152> and\nYoo (2019) <doi:10.1016/j.jkss.2019.02.001>. The method by Yoo\nand Cook (2008) is based on non-parametric ordinary least\nsquares, but the model-based approaches are done through\nmaximum likelihood estimation. For two model-based response\ndimension reduction methods called principal fitted response\nreduction and unstructured principal fitted response reduction,\nchi-squared tests are provided for determining the dimension of\nthe response subspace.",
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