Package: SeasEpi 0.0.3

SeasEpi: Spatiotemporal Modeling of Seasonal Infectious Disease

Spatiotemporal individual-level model of seasonal infectious disease transmission within the Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) framework are applied to model seasonal infectious disease transmission. This package employs a likelihood based Monte Carlo Expectation Conditional Maximization (MCECM) algorithm for estimating model parameters. In addition to model fitting and parameter estimation, the package offers functions for calculating AIC using real pandemic data and conducting simulation studies customized to user-specified model configurations.

Authors:Amin Abed [aut, cre, cph], Mahmoud Torabi [ths], Zeinab Mashreghi [ths]

SeasEpi_0.0.3.tar.gz
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SeasEpi_0.0.3.tgz(r-4.6-any)SeasEpi_0.0.3.tgz(r-4.5-any)
SeasEpi_0.0.3.tar.gz(r-4.7-any)SeasEpi_0.0.3.tar.gz(r-4.6-any)
SeasEpi_0.0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
SeasEpi/json (API)

# Install 'SeasEpi' in R:
install.packages('SeasEpi', repos = c('https://amin-abed.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.30 score 161 downloads 2 exports 6 dependencies

Last updated from:0f18d27bcf. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK109
source / vignettesOK159
linux-release-x86_64OK110
macos-release-arm64OK139
macos-oldrel-arm64OK134
windows-develOK79
windows-releaseOK74
windows-oldrelOK60
wasm-releaseOK97

Exports:SeasEpi_Par_EstSeasEpi_Sim_Par_Est

Dependencies:batchmeansMASSmvtnormngspatialRcppRcppArmadillo

Readme and manuals

Help Manual

Help pageTopics
Hypothetical Datasetsadjacency_matrix data
SeasEpi for Real DataSeasEpi_Par_Est
SeasEpi for a Simulation StudySeasEpi_Sim_Par_Est