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RtForecastR

Real-Time Effective Reproduction Number Estimation and Forecasting

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.

Versions across snapshots

VersionRepositoryFileSize
0.1.0 rolling linux/jammy R-4.5 RtForecastR_0.1.0.tar.gz 99.6 KiB
0.1.0 rolling linux/noble R-4.5 RtForecastR_0.1.0.tar.gz 99.6 KiB
0.1.0 rolling source/ R- RtForecastR_0.1.0.tar.gz 39.0 KiB
0.1.0 latest linux/jammy R-4.5 RtForecastR_0.1.0.tar.gz 99.6 KiB
0.1.0 latest linux/noble R-4.5 RtForecastR_0.1.0.tar.gz 99.6 KiB
0.1.0 latest source/ R- RtForecastR_0.1.0.tar.gz 39.0 KiB
0.1.0 2026-04-23 source/ R- RtForecastR_0.1.0.tar.gz 0 B

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