rts2: Real-Time Disease Surveillance
Supports modelling real-time case data to facilitate the real-time
surveillance of infectious disease. A simple grid class structure is provided to generate a computational grid over
an area of interest with methods to map covariates between geographies. An approximate log-Gaussian Cox Process
model is fit using 'rstan' or 'cmdstanr' and provides output and analysis as 'sf' objects for simple visualisation.
'cmdstanr' can be downloaded at <https://mc-stan.org/cmdstanr/>. Log-Gaussian Cox Processes are described by
Diggle et al. (2013) <doi:10.1214/13-STS441> and we use the low-rank approximation for Gaussian processes
described by Solin and Särkkä (2020) <doi:10.1007/s11222-019-09886-w> and Riutort-Mayol (2020) <arXiv:2004.11408>.
Version: |
0.3 |
Depends: |
R (≥ 3.4.0) |
Imports: |
methods, R6, Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), rstantools (≥ 2.1.1), sf (≥ 1.0-5), lubridate |
LinkingTo: |
BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), StanHeaders (≥
2.18.0) |
Suggests: |
cmdstanr (≥ 0.4.0), testthat |
Published: |
2022-03-21 |
Author: |
Sam Watson [aut,
cre] |
Maintainer: |
Sam Watson <s.i.watson at bham.ac.uk> |
License: |
CC BY-SA 4.0 |
URL: |
http://www.sam-watson.xyz/vignette.html |
NeedsCompilation: |
yes |
SystemRequirements: |
GNU make |
CRAN checks: |
rts2 results |
Documentation:
Downloads:
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