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).
- Version0.3.0
- R version≥ 3.5.0
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- surveil citation info
- Last release07/08/2024
Documentation
Team
Connor Donegan
Insights
Last 30 days
This package has been downloaded 260 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 8 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 3,612 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Jul 23, 2024 with 50 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Imports12 packages
- Suggests3 packages
- Linking To6 packages