individual

individual: An R package for individual-based epidemiological models - Published in JOSS (2021)

https://github.com/mrc-ide/individual

Science Score: 98.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 4 DOI reference(s) in README and JOSS metadata
  • Academic publication links
    Links to: joss.theoj.org
  • Committers with academic emails
    2 of 6 committers (33.3%) from academic institutions
  • Institutional organization owner
    Organization mrc-ide has institutional domain (www.imperial.ac.uk)
  • JOSS paper metadata
    Published in Journal of Open Source Software

Keywords

agent-based-modeling epidemiology individual-based-modelling infectious-diseases r r-package rstats

Scientific Fields

Earth and Environmental Sciences Physical Sciences - 40% confidence
Last synced: 6 months ago · JSON representation

Repository

R Package for individual based epidemiological models

Basic Info
Statistics
  • Stars: 30
  • Watchers: 5
  • Forks: 17
  • Open Issues: 9
  • Releases: 25
Topics
agent-based-modeling epidemiology individual-based-modelling infectious-diseases r r-package rstats
Created about 6 years ago · Last pushed 6 months ago
Metadata Files
Readme Changelog License

README.md

Individual

R build status codecov.io CRAN License: MIT DOI <!-- badges: end -->

An R package for specifying and simulating individual-based models.

This package is designed to:

  1. encourage clear and testable components for defining your individual-based models, and
  2. provide memory efficient, fast code for executing your model

Installation

The package can be installed from github using the "remotes" library

R library('remotes') install_github('mrc-ide/individual')

Alternatively you can install individual directly from CRAN, but be aware that the CRAN version may not be the most recent version of the package:

R install.packages("individual")

For development it is most convenient to run the code from source. You can install the dependencies in RStudio by opening the project and selecting "Build" > "Install and Restart"

Command line users can execute:

R library('remotes') install_deps('.', dependencies = TRUE)

Docker users can build a minimal image with

bash docker build . -f docker/Dockerfile -t [your image name]

Or if you would like devtools and documentation tools you can run

bash docker build . -f docker/Dockerfile.dev -t [your image name]

Usage

We recommend first reading vignette("Tutorial") which describes how to simulate a simple SIR model in "individual", and later vignette("API") which describes in detail how to use the data structures in "individual" to build more complicated models. If you are running into performance issues, learn more about how to speed up your model in vignette("Performance").

Statement of need

Individual-based models are important tools for infectious disease epidemiology, but practical use requires an implementation that is both comprehensible so that code may be maintained and adapted, and fast. "individual" is an R package which provides users a set of primitive classes using the R6 class system that define elements common to many tasks in infectious disease modeling. Using R6 classes helps ensure that methods invoked on objects are appropriate for that object type, aiding in testing and maintenance of models programmed using "individual". Computation is carried out in C++ using Rcpp to link to R, helping achieve good performance for even complex models.

"individual" provides a unique method to specify individual-based models compared to other agent/individual-based modeling libraries, where users specify a type for agents, which are subsequently stored in an array or other data structure. In "individual", users instead instantiate a object for each variable which describes some aspect of state, using the appropriate R6 class. Finding subsets of individuals with particular combinations of state variables for further computation can be efficiently accomplished with set operations, using a custom bitset class implemented in C++. Additionally, the software makes no assumptions on the types of models that may be simulated (e.g. mass action, network), and updates are performed on a discrete time step.

We hope our software is useful to infectious disease modellers, ecologists, and others who are interested in individual-based modeling in R.

Contributing

Thank you! Please refer to the vignette on vignette("Contributing") for info on how to contribute :)

Alternatives

Non R Software

Non R Software for Epi

General R Packages

R based DES

R based IBMs

R based Epi

Owner

  • Name: MRC Centre for Global Infectious Disease Analysis
  • Login: mrc-ide
  • Kind: organization
  • Location: London, UK

MRC Centre hosted within the Department of Infectious Disease Epidemiology at Imperial College London

JOSS Publication

individual: An R package for individual-based epidemiological models
Published
October 07, 2021
Volume 6, Issue 66, Page 3539
Authors
Giovanni D. Charles
MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK.
Sean L. Wu ORCID
Division of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, CA 94720, USA
Editor
Mikkel Meyer Andersen ORCID
Tags
epidemiology individual based agent based infectious disease simulation stochastic

GitHub Events

Total
  • Issue comment event: 4
  • Push event: 6
  • Pull request review event: 7
  • Pull request review comment event: 2
  • Pull request event: 4
  • Create event: 1
Last Year
  • Issue comment event: 4
  • Push event: 6
  • Pull request review event: 7
  • Pull request review comment event: 2
  • Pull request event: 4
  • Create event: 1

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 486
  • Total Committers: 6
  • Avg Commits per committer: 81.0
  • Development Distribution Score (DDS): 0.539
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
slwu89 1****9 224
Giovanni Charles g****s@g****m 212
Paul Lietar p****3@i****k 30
Pietro Monticone 3****e 12
Peter Winskill p****l@i****k 7
Darío Hereñú m****a@g****m 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 30
  • Total pull requests: 71
  • Average time to close issues: 4 months
  • Average time to close pull requests: 4 days
  • Total issue authors: 6
  • Total pull request authors: 5
  • Average comments per issue: 1.73
  • Average comments per pull request: 0.9
  • Merged pull requests: 67
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 3.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • giovannic (20)
  • slwu89 (5)
  • cwhittaker1000 (2)
  • OJWatson (2)
  • pwinskill (2)
  • marialma (1)
  • richfitz (1)
Pull Request Authors
  • giovannic (42)
  • slwu89 (26)
  • plietar (25)
  • pwinskill (4)
  • cwhittaker1000 (2)
  • kant (1)
Top Labels
Issue Labels
enhancement (14) small (7) bug (6) medium (6) large (4) admin (2) documentation (1) good first issue (1)
Pull Request Labels
enhancement (5) bug (2) documentation (1)

Packages

  • Total packages: 1
  • Total downloads: unknown
  • Total dependent packages: 0
  • Total dependent repositories: 7
  • Total versions: 5
cran.r-project.org: individual

Framework for Specifying and Simulating Individual Based Models

  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 7
  • Downloads: 0
Rankings
Forks count: 6.0%
Stargazers count: 11.3%
Dependent repos count: 11.8%
Average: 29.7%
Dependent packages count: 29.8%
Downloads: 89.7%
Last synced: 6 months ago

Dependencies

.github/workflows/R-CMD-check.yaml actions
  • actions/cache v2 composite
  • actions/checkout v2 composite
  • actions/upload-artifact main composite
  • r-lib/actions/setup-pandoc v2 composite
  • r-lib/actions/setup-r v2 composite
.github/workflows/pkgdown.yml actions
  • actions/cache v2 composite
  • actions/checkout v2 composite
  • r-lib/actions/setup-pandoc v2 composite
  • r-lib/actions/setup-r v2 composite
.github/workflows/test_coverage.yml actions
  • actions/cache v2 composite
  • actions/checkout v2 composite
  • r-lib/actions/setup-pandoc v2 composite
  • r-lib/actions/setup-r v2 composite
DESCRIPTION cran
  • R6 * imports
  • Rcpp * imports
  • bench * suggests
  • ggplot2 * suggests
  • knitr * suggests
  • mockery * suggests
  • pkgdown * suggests
  • rmarkdown * suggests
  • testthat >= 2.1.0 suggests
  • xml2 * suggests
docker/Dockerfile docker
  • rocker/r-ver 4.0.2 build