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  "Title": "Real-Time Effective Reproduction Number Estimation and\nForecasting",
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  "Authors@R": "c(\nperson(\"Raj\", \"Subedi\", email = \"rajsubediresearch@gmail.com\", role = c(\"aut\", \"cre\", \"cph\"),\ncomment = \"Copyright holder for all files except epiFilter.R, epiSmoother.R, and the original recursPredict.R logic (see Kris V. Parag entry); author of R/recursPredict.R's configurable-grid maxI extension and R/recursPredictQuantiles.R\"),\nperson(\"Kris V.\", \"Parag\", role = c(\"ctb\", \"cph\"),\ncomment = \"Author/copyright holder of the original EpiFilter algorithm (epiFilter, epiSmoother, recursPredict); files R/epiFilter.R, R/epiSmoother.R and R/recursPredict.R are unmodified or lightly modified ports of that work, released under GPL-3\"))",
  "Description": "Filtered (real-time/causal) and smoothed (retrospective)\nestimation of the time-varying effective reproduction number\n(Rt) from case-count time series, using the EpiFilter algorithm\nof Parag (2021) <doi:10.1371/journal.pcbi.1009347>, together\nwith a one-step-ahead in-sample prediction check, a genuine\nout-of-sample one-step forecast with predictive intervals,\nelimination probability P(Rt < 1), and forecast calibration\nmetrics (mean absolute error, mean squared error, root mean\nsquared error, empirical coverage, and the weighted interval\nscore of Bracher et al. (2021)\n<doi:10.1371/journal.pcbi.1008618>). Disease-agnostic: works\nfor any pathogen given a known generation interval.",
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