Package: epichains 0.1.1.9000

James M. Azam

epichains: Simulating and Analysing Transmission Chain Statistics Using Branching Process Models

Provides methods to simulate and analyse the size and length of branching processes with an arbitrary offspring distribution. These can be used, for example, to analyse the distribution of chain sizes or length of infectious disease outbreaks, as discussed in Farrington et al. (2003) <doi:10.1093/biostatistics/4.2.279>.

Authors:James M. Azam [aut, cre, cph], Sebastian Funk [aut, cph], Flavio Finger [aut], Zhian N. Kamvar [ctb], Hugo Gruson [ctb, rev], Karim Mané [rev], Pratik Gupte [rev], Joshua W. Lambert [rev]

epichains_0.1.1.9000.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
epichains/json (API)

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

Bug tracker:https://github.com/epiverse-trace/epichains/issues

Pkgdown/docs site:https://epiverse-trace.github.io

Datasets:
  • covid19_sa - COVID-19 Data Repository for South Africa

On CRAN:

Conda:

branching-processesepidemic-dynamicsepidemic-modellingepidemic-simulationsepidemiologyepidemiology-modelsoutbreak-simulatortransmission-chaintransmission-chain-reconstruction

6.80 score 10 stars 30 scripts 313 downloads 6 exports 2 dependencies

Last updated from:17c8b695a5. Checks:9 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develOK124
source / vignettesOK252
linux-releaseOK119
macos-releaseOK97
macos-oldrelOK82
windows-develOK112
windows-releaseOK77
windows-oldrelOK65
wasm-releaseOK96

Exports:dborellikelihoodrborelrgborelsimulate_chain_statssimulate_chains

Dependencies:backportscheckmate

Design Principles for {epichains}
Scope | Design decisions | Simulation functions | likelihood estimation | Naming and documentation style | Dependencies | Development journey
Rendered fromdesign-principles.Rmdusingknitr::rmarkdown

Last update: 2024-10-17
Started: 2024-05-15

Modelling disease control interventions
Reducing the strength of transmission | Population-wide control | Individual-level control. | Preventing superspreading events | Truncating the generation interval | References
Rendered frominterventions.Rmdusingknitr::rmarkdown

Last update: 2024-10-17
Started: 2023-11-10

Projecting infectious disease incidence: a COVID-19 example
Overview | Data | Setting up the inputs | Onset times | Generation time | Offspring distribution | Simulation controls | Modelling assumptions | Running the simulations | Post-processing | Visualization | References
Rendered fromprojecting_incidence.Rmdusingknitr::rmarkdown

Last update: 2024-10-17
Started: 2023-03-06

Theoretical background for epichains
Branching processes | Simulation | Summary statistics | Inference | Size and length distributions for some offspring distributions | Negative binomial and special cases | Size distributions | Length distributions | Gamma-Borel mixture | Numerical approximations of chain size and length distributions
Rendered fromtheoretical_background.Rmdusingknitr::rmarkdown

Last update: 2024-07-10
Started: 2023-09-25

Literature on branching process applications
Bibliography
Rendered frombranching_process_literature.Rmdusingknitr::rmarkdown

Last update: 2024-06-21
Started: 2023-09-25

Getting started with epichains
Transmission chains likelihoods | Use case | What we have | What we assume | likelihood() | Joint and individual log-likelihoods | Observation probability | How likelihood() works | Transmission chain simulation | simulate_chains() | simulate_chain_stats() | S3 Methods | Summarising | Aggregating | Plotting | Some notes about interoperability between and objects | References
Rendered fromepichains.Rmdusingknitr::rmarkdown

Last update: 2024-05-23
Started: 2023-09-02