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  "Title": "Quantifying Ecological Memory in Palaeoecological Datasets and\nOther Long Time-Series",
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  "Authors@R": "person(given = \"Blas M.\",\nfamily = \"Benito\", ,\nemail = \"blasbenito@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0001-5105-7232\"))",
  "Description": "Quantifies ecological memory in long time-series using\nRandom Forest models ('Benito', 'Gil-Romera', and 'Birks' 2019\n<doi:10.1111/ecog.04772>) fitted with 'ranger' (Wright and\nZiegler 2017 <doi:10.18637/jss.v077.i01>). Ecological memory is\nassessed by modeling a response variable as a function of\nlagged predictors, distinguishing endogenous memory (lagged\nresponse) from exogenous memory (lagged environmental drivers).\nDesigned for palaeoecological datasets and simulated pollen\ncurves from 'virtualPollen', but applicable to any long\ntime-series with environmental drivers and a biotic response.",
  "License": "MIT + file LICENSE",
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  "Date/Publication": "2026-02-10 12:56:58 UTC",
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