{
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  "Title": "Data Thinning of Species Occurrences in Environmental Space",
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  "Authors@R": "c(\nperson(\"Paanwaris\", \"Paansri\",\nrole = c(\"cre\", \"aut\"),\nemail = \"paanwaris@vt.edu\",\ncomment = c(ORCID = \"0000-0001-9992-098X\")),\nperson(\"Luis E.\", \"Escobar\",\nrole = c(\"aut\", \"ctb\"),\nemail = \"escobar1@vt.edu\",\ncomment = c(ORCID = \"0000-0001-5735-2750\")))",
  "Maintainer": "Paanwaris Paansri <paanwaris@vt.edu>",
  "Description": "A suite of tools to mitigate sampling bias in species\noccurrence records by thinning data in the environmental space\n(E-space). This process can improve the accuracy and precision\nof species distribution models (SDM, also known as ecological\nniche models, ENM). The package offers a data-driven protocol\nto determine thinning parameters using kernel-density bandwidth\nselection. Two thinning methods are provided (stochastic and\ndeterministic) to reduce over-sampled environmental conditions\nand down-weight outlier observations. The name 'bean' reflects\nthe core principle of the method: each 'pod' (a grid cell in\nE-space) is allowed to contain only a limited number of 'beans'\n(occurrence points). See Silverman (1986,\nISBN:978-0-412-24620-3) and Rousseeuw and Leroy (2003,\nISBN:978-0-471-48855-2) for the underlying statistical methods.",
  "License": "MIT + file LICENSE",
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