Real-Time Effective Reproduction Number Estimation and Forecasting R package RtForecastR version 0.1.1 Filtered (real-time/causal) and smoothed (retrospective) estimation of the time-varying effective reproduction number (Rt) from case-count time series, using the EpiFilter algorithm of Parag (2021) <doi:10.1371/journal.pcbi.1009347>, together with a one-step-ahead in-sample prediction check, a genuine out-of-sample one-step forecast with predictive intervals, elimination probability P(Rt < 1), and forecast calibration metrics (mean absolute error, mean squared error, root mean squared error, empirical coverage, and the weighted interval score of Bracher et al. (2021) <doi:10.1371/journal.pcbi.1008618>). Disease-agnostic: works for any pathogen given a known generation interval. Software Depends: R (>= 3.5) Imports: graphics, grDevices, stats, utils Suggests: testthat (>= 3.0.0), knitr, rmarkdown Raj Subedi <rajsubediresearch@gmail.com> Comprehensive R Archive Network (CRAN) Raj Subedi [aut, cre, cph] (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), Kris V. Parag [ctb, cph] (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) GPL-3 2026-08-21 application/tgz https://CRAN.R-project.org/package=RtForecastR doi:10.32614/CRAN.package.RtForecastR en-US