Package: hhdynamics 1.3.3

hhdynamics: Fitting Household Transmission Model to Estimate Household Transmission Dynamics of Influenza

A Bayesian household transmission model to estimate household transmission dynamics, with accounting for infection from community and tertiary cases.

Authors:Tim Tsang [aut, cre]

hhdynamics_1.3.3.tar.gz
hhdynamics_1.3.3.zip(r-4.7-x86_64)hhdynamics_1.3.3.zip(r-4.7-arm64)hhdynamics_1.3.3.zip(r-4.6-x86_64)hhdynamics_1.3.3.zip(r-4.6-arm64)hhdynamics_1.3.3.zip(r-4.5-x86_64)
hhdynamics_1.3.3.tgz(r-4.6-x86_64)hhdynamics_1.3.3.tgz(r-4.6-arm64)hhdynamics_1.3.3.tgz(r-4.5-x86_64)hhdynamics_1.3.3.tgz(r-4.5-arm64)
hhdynamics_1.3.3.tar.gz(r-4.7-arm64)hhdynamics_1.3.3.tar.gz(r-4.7-x86_64)hhdynamics_1.3.3.tar.gz(r-4.6-arm64)hhdynamics_1.3.3.tar.gz(r-4.6-x86_64)
hhdynamics_1.3.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
hhdynamics/json (API)

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

Bug tracker:https://github.com/timktsang/hhdynamics/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • inputdata - Example of input data
  • para - Example of parameter vector for the main model
  • SI - Example of serial interval distribution of influenza

On CRAN:

Conda:

openblascpp

4.48 score 5 scripts 334 downloads 12 exports 3 dependencies

Last updated from:4e3a025443. Checks:15 OK. Indexed: yes.

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linux-devel-arm64OK115
linux-devel-x86_64OK143
source / vignettesOK234
linux-release-arm64OK108
linux-release-x86_64OK131
macos-release-arm64OK88
macos-release-x86_64OK359
macos-oldrel-arm64OK118
macos-oldrel-x86_64OK267
windows-devel-arm64OK250
windows-devel-x86_64OK173
windows-release-arm64OK242
windows-release-x86_64OK127
windows-oldrel-x86_64OK115
wasm-releaseOK100

Exports:create_wide_datahousehold_dynamicsplot_attack_rateplot_covariatesplot_diagnosticsplot_householdplot_transmissionrun_MCMCsimulate_datatable_attack_ratestable_covariatestable_parameters

Dependencies:RcppRcppArmadilloRcppParallel

Getting Started with hhdynamics
Overview | Data format | Serial interval | Fitting the model | Inspecting results | Quick overview | Parameter estimates | Posterior means | Accessing MCMC output | Visualization | MCMC diagnostics | Transmission probability over time | Covariate effects (forest plot) | Secondary attack rates | Household timeline | Summary tables | Covariate effects | Input validation | Missing data handling | Missing onset times | Missing covariates | Estimating the serial interval | Simulation
Rendered fromhhdynamics-intro.Rmdusingknitr::rmarkdown

Last update: 2026-03-23
Started: 2026-03-19

Statistical Methodology
Overview | Model | Study design | Infection hazard | Covariates | Likelihood | Individual likelihood | Full likelihood | Prior distributions | MCMC algorithm | Metropolis-Hastings sampler | Adaptive proposal tuning | Data augmentation | Serial interval estimation | Convergence diagnostics | Recommended MCMC settings | Interpretation of parameters | Base parameters | Covariate effects | Serial interval parameters | Computational details | References
Rendered fromhhdynamics-methodology.Rmdusingknitr::rmarkdown

Last update: 2026-03-23
Started: 2026-03-23