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  "Title": "Concurrent Regression and History Index Models for Functional\nData",
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  "Authors@R": "c(person(given = \"Su I\",\nfamily = \"Iao\",\nrole = c(\"aut\", \"cre\"),\nemail = \"siao@ucdavis.edu\"),\nperson(given = \"Satarupa\",\nfamily = \"Bhattacharjee\",\nrole = \"aut\"),\nperson(given = \"Yaqing\",\nfamily = \"Chen\",\nrole = \"aut\"),\nperson(given = \"Changbo\",\nfamily = \"Zhu\",\nrole = \"aut\"),\nperson(given = \"Han\",\nfamily = \"Chen\",\nrole = \"aut\"),\nperson(given = \"Yidong\",\nfamily = \"Zhou\",\nrole = \"aut\"),\nperson(given = \"Álvaro\",\nfamily = \"Gajardo\",\nrole = \"aut\"),\nperson(given = \"Poorbita\",\nfamily = \"Kundu\",\nrole = \"aut\"),\nperson(given = \"Hang\",\nfamily = \"Zhou\",\nrole = \"aut\"),\nperson(given = \"Hans-Georg\",\nfamily = \"Müller\",\nrole = c(\"cph\", \"ths\", \"aut\")))",
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  "Description": "Provides an implementation of concurrent or varying\ncoefficient regression methods for functional data. The\nimplementations are done for both dense and sparsely observed\nfunctional data. Pointwise confidence bands can be constructed\nfor each case. Further, the influence of past predictor values\nare modeled by a smooth history index function, while the\neffects on the response are described by smooth varying\ncoefficient functions, which are very useful in analyzing real\ndata such as COVID data. References: Yao, F., Müller, H.G.,\nWang, J.L. (2005) <doi: 10.1214/009053605000000660>. Sentürk,\nD., Müller, H.G. (2010) <doi: 10.1198/jasa.2010.tm09228>.",
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  "Repository": "https://functionaldata.r-universe.dev",
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      "title": "Functional Concurrent Regression with Lag Model",
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      "title": "Functional Concurrent Regression using 2D smoothing",
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      "title": "Bootstrap pointwise confidence intervals for the coefficient functions in functional concurrent regression for sparsely observed data.",
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