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  "Title": "Robust Angle Based Joint and Individual Variation Explained",
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  "Authors@R": "c(\nperson(given = \"Erica\",\nfamily = \"Ponzi\",\nrole = c(\"aut\", \"cre\"),\nemail = \"erica.ponzi@medisin.uio.no\"),\nperson(given = \"Abhik\",\nfamily = \"Ghosh\",\nrole = c(\"aut\"),\nemail = \"abhik.ghosh.stat@gmail.com\"))",
  "Description": "A robust alternative to the aJIVE (angle based Joint and\nIndividual Variation Explained) method (Feng et al 2018:\n<doi:10.1016/j.jmva.2018.03.008>) for the estimation of joint\nand individual components in the presence of outliers in\nmulti-source data. It decomposes the multi-source data into\njoint, individual and residual (noise) contributions. The\ndecomposition is robust to outliers and noise in the data. The\nmethod is illustrated in Ponzi et al (2021) <arXiv:2101.09110>.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
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  "Repository": "https://ericaponzi.r-universe.dev",
  "Date/Publication": "2021-01-28 12:54:39 UTC",
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    "User": "root"
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  "Author": "Erica Ponzi [aut, cre],\nAbhik Ghosh [aut]",
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