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  "Title": "Penalized Poisson Pseudo Maximum Likelihood Regression",
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  "Authors@R": "c(\nperson(given = \"Diego\",\nfamily = \"Ferreras Garrucho\",\nrole = \"aut\",\nemail = \"d.ferreras-garrucho@lse.ac.uk\"),\nperson(given = \"Tom\",\nfamily = \"Zylkin\",\nrole = \"aut\",\nemail = \"tzylkin@richmond.edu\"),\nperson(given = \"Joao\",\nfamily = \"Cruz\",\nrole = c(\"cre\",\"ctb\"),\nemail = \"joaocruz@iseg.ulisboa.pt\"),\nperson(given = \"Nicolas\",\nfamily = \"Apfel\",\nrole = \"ctb\",\nemail = \"n.apfel@soton.ac.uk\")\n)",
  "Description": "A set of tools that enables efficient estimation of\npenalized Poisson Pseudo Maximum Likelihood regressions, using\nlasso or ridge penalties, for models that feature one or more\nsets of high-dimensional fixed effects. The methodology is\nbased on Breinlich, Corradi, Rocha, Ruta, Santos Silva, and\nZylkin (2021) <http://hdl.handle.net/10986/35451> and takes\nadvantage of the method of alternating projections of Gaure\n(2013) <doi:10.1016/j.csda.2013.03.024> for dealing with HDFE,\nas well as the coordinate descent algorithm of Friedman, Hastie\nand Tibshirani (2010) <doi:10.18637/jss.v033.i01> for fitting\nlasso regressions. The package is also able to carry out\ncross-validation and to implement the plugin lasso of Belloni,\nChernozhukov, Hansen and Kozbur (2016)\n<doi:10.1080/07350015.2015.1102733>.",
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  "Author": "Diego Ferreras Garrucho [aut],\nTom Zylkin [aut],\nJoao Cruz [cre, ctb],\nNicolas Apfel [ctb]",
  "Maintainer": "Joao Cruz <joaocruz@iseg.ulisboa.pt>",
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