Package: surveil 0.3.0
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).
Authors:
surveil_0.3.0.tar.gz
surveil_0.3.0.zip(r-4.5)surveil_0.3.0.zip(r-4.4)surveil_0.3.0.zip(r-4.3)
surveil_0.3.0.tgz(r-4.4-arm64)surveil_0.3.0.tgz(r-4.3-arm64)
surveil_0.3.0.tar.gz(r-4.5-noble)surveil_0.3.0.tar.gz(r-4.4-noble)
surveil.pdf |surveil.html✨
surveil/json (API)
NEWS
| # Install 'surveil' in R: |
| install.packages('surveil', repos = c('https://connordonegan.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/connordonegan/surveil/issues
bayesian-statisticscancerhealth-equitypublic-healthrstan
Last updated 5 months agofrom:847d19fc67. Checks:OK: 1 NOTE: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 05 2024 |
| R-4.5-win-x86_64 | NOTE | Nov 05 2024 |
| R-4.5-linux-x86_64 | NOTE | Nov 05 2024 |
| R-4.4-win-x86_64 | NOTE | Nov 05 2024 |
| R-4.4-mac-aarch64 | NOTE | Nov 05 2024 |
| R-4.3-win-x86_64 | NOTE | Nov 05 2024 |
| R-4.3-mac-aarch64 | NOTE | Nov 05 2024 |
Exports:apcgroup_difflkjnormalstan_rwstandardizetheiltheil2waic
Dependencies:abindarrayhelpersbackportsBHcallrcheckmateclicodacolorspacecpp11descdistributionaldplyrfansifarvergenericsggdistggplot2gluegridExtragtableinlineisobandlabelinglatticelifecycleloomagrittrMASSMatrixmatrixStatsmgcvmunsellnlmenumDerivpillarpkgbuildpkgconfigposteriorprocessxpspurrrquadprogQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelrlangrstanrstantoolsscalesStanHeadersstringistringrsvUnittensorAtibbletidybayestidyrtidyselectutf8vctrsviridisLitewithr
Age-standardized rates
Rendered fromage-standardization.Rmdusingknitr::rmarkdownon Nov 05 2024.Last update: 2024-07-08
Started: 2022-03-29
MCMC with surveil
Rendered fromsurveil-mcmc.Rmdusingknitr::rmarkdownon Nov 05 2024.Last update: 2024-07-08
Started: 2022-07-29
Measuring health inequality
Rendered frommeasuring-inequality.Rmdusingknitr::rmarkdownon Nov 05 2024.Last update: 2024-07-08
Started: 2024-07-08
Using surveil for public health research
Rendered fromsurveil-demo.Rmdusingknitr::rmarkdownon Nov 05 2024.Last update: 2024-07-08
Started: 2022-07-29
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| The 'surveil' package | surveil-package surveil |
| Annual and cumulative percent change | apc apc.stand_surveil apc.surveil |
| US cancer incidence by age, 1999-2017 | cancer |
| Measures of pairwise inequality | group_diff group_diff.list group_diff.surveil |
| Colorectal cancer incidence by Texas MSA, 1999-2017, ages 50-79 | msa |
| Methods for fitted 'surveil' models | plot.list plot.surveil print.surveil |
| Methods for Theil's index | plot.theil plot.theil_list print.theil print.theil_list |
| Methods for APC objects | plot.apc print.apc |
| Methods for age-standardized rates | plot.stand_surveil print.stand_surveil |
| Prior distributions | lkj normal priors |
| Time series models for mortality and disease incidence | stan_rw |
| 2000 U.S. standard million population | standard |
| Age-standardized rates | standardize |
| Methods for 'surveil_diff' objects | plot.surveil_diff print.surveil_diff surveil_diff |
| Theil's inequality index | theil theil.list theil.surveil theil2 |
| Widely Applicable Information Criteria | waic |
