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  "Title": "Exactly Computing the Tail of the Poisson-Binomial Distribution",
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  "Authors@R": "c(\nperson(\"Andrew\", \"Lee\", role = c(\"aut\", \"cre\"), email = \"andrewl@maths.usyd.edu.au\"),\nperson(\"Noah\", \"Peres\", role = c(\"aut\", \"ctb\"), email = \"noahp@maths.usyd.edu.au\"),\nperson(\"Uri\", \"Keich\", role = c(\"aut\", \"ctb\"), email = \"uri.keich@sydney.edu.au\"),\nperson(\"Alexander\", \"Mukhin\", role = c(\"cph\"), email = \"alexander.i.mukhin@gmail.com\"))",
  "Author": "Andrew Lee [aut, cre], Noah Peres [aut, ctb], Uri Keich [aut,\nctb], Alexander Mukhin [cph]",
  "Maintainer": "Andrew Lee <andrewl@maths.usyd.edu.au>",
  "Description": "An exact method for computing the Poisson-Binomial\nDistribution (PBD). The package provides a function for\ngenerating a random sample from the PBD, as well as two\ndistinct approaches for computing the density, distribution,\nand quantile functions of the PBD. The first method uses\ndirect-convolution, or a dynamic-programming approach which is\nnumerically stable but can be slow for a large input due to its\nquadratic complexity. The second method is much faster on large\ninputs thanks to its use of Fast Fourier Transform (FFT) based\nconvolutions. Notably in this case the package uses an\nexponential shift to practically guarantee the relative\naccuracy of the computation of an arbitrarily small tail of the\nPBD -- something that FFT-based methods often struggle with.\nThis ShiftConvolvePoiBin method is described in Peres, Lee and\nKeich (2020) <arXiv:2004.07429> where it is also shown to be\ncompetitive with the fastest implementations for exactly\ncomputing the entire Poisson-Binomial distribution.",
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