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surveil

Time Series Models for Disease Surveillance

Fits time trend models for routine disease surveillance tasks and returns probability distributions for a variety of quantities of interest, including age-standardized rates, period and cumulative percent change, and measures of health inequality. The models are appropriate for count data such as disease incidence and mortality data, employing a Poisson or binomial likelihood and the first-difference (random-walk) prior for unknown risk. Optionally add a covariance matrix for multiple, correlated time series models. Inference is completed using Markov chain Monte Carlo via the Stan modeling language. References: Donegan, Hughes, and Lee (2022) <doi:10.2196/34589>; Stan Development Team (2021) <https://mc-stan.org>; Theil (1972, ISBN:0-444-10378-3).

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VersionRepositoryFileSize
0.3.0 rolling linux/jammy R-4.5 surveil_0.3.0.tar.gz 1.5 MiB
0.3.0 rolling linux/noble R-4.5 surveil_0.3.0.tar.gz 1.5 MiB
0.3.0 rolling source/ R- surveil_0.3.0.tar.gz 625.9 KiB
0.3.0 latest linux/jammy R-4.5 surveil_0.3.0.tar.gz 1.5 MiB
0.3.0 latest linux/noble R-4.5 surveil_0.3.0.tar.gz 1.5 MiB
0.3.0 latest source/ R- surveil_0.3.0.tar.gz 625.9 KiB
0.3.0 2026-04-26 source/ R- surveil_0.3.0.tar.gz 625.9 KiB
0.3.0 2026-04-23 source/ R- surveil_0.3.0.tar.gz 625.9 KiB
0.3.0 2026-04-09 windows/windows R-4.5 surveil_0.3.0.zip 1.7 MiB
0.3.0 2025-04-20 source/ R- surveil_0.3.0.tar.gz 625.9 KiB

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