Package: GDILM.SEIRS 0.0.2
Amin Abed
GDILM.SEIRS: Spatial Modeling of Infectious Disease with Reinfection
Geographically Dependent Individual Level Models (GDILMs) within the Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) framework are applied to model infectious disease transmission, incorporating reinfection dynamics. This package employs a likelihood based Monte Carlo Expectation Conditional Maximization (MCECM) algorithm for estimating model parameters. It also provides tools for GDILM fitting, parameter estimation, AIC calculation on real pandemic data, and simulation studies customized to user-defined model settings.
Authors:
GDILM.SEIRS_0.0.2.tar.gz
GDILM.SEIRS_0.0.2.tar.gz(r-4.5-noble)GDILM.SEIRS_0.0.2.tar.gz(r-4.4-noble)
GDILM.SEIRS_0.0.2.tgz(r-4.4-emscripten)GDILM.SEIRS_0.0.2.tgz(r-4.3-emscripten)
GDILM.SEIRS.pdf |GDILM.SEIRS.html✨
GDILM.SEIRS/json (API)
| # Install 'GDILM.SEIRS' in R: |
| install.packages('GDILM.SEIRS', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- adjacency_matrix - Hypothetical Datasets
- data - Hypothetical Datasets
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 18 days agofrom:590357270f. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 08 2024 |
| R-4.5-linux | OK | Dec 08 2024 |
Exports:GDILM_SEIRS_Par_EstGDILM_SEIRS_Sim_Par_Est
Dependencies:batchmeansMASSmvtnormngspatialRcppRcppArmadillo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Hypothetical Datasets | adjacency_matrix data |
| GDILM SEIRS for Real Data | GDILM_SEIRS_Par_Est |
| GDILM SEIRS for a Simulation Study | GDILM_SEIRS_Sim_Par_Est |
