{"id":32207247,"url":"https://github.com/uofuepibio/epiworldr","last_synced_at":"2026-01-16T10:56:14.989Z","repository":{"id":45407464,"uuid":"513615327","full_name":"UofUEpiBio/epiworldR","owner":"UofUEpiBio","description":"A general framework for quick epidemiological ABM models","archived":false,"fork":false,"pushed_at":"2025-10-21T22:33:02.000Z","size":61925,"stargazers_count":11,"open_issues_count":34,"forks_count":3,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-22T05:50:01.853Z","etag":null,"topics":["abm","agent-based-modeling","covid-19","epidemics","epidemiology","r-package","r-programming","rpack","rpkg","seir","seir-model","simulation","sir","sir-model"],"latest_commit_sha":null,"homepage":"https://uofuepibio.github.io/epiworldR/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/UofUEpiBio.png","metadata":{"files":{"readme":"README.md","changelog":"NEWS.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2022-07-13T17:36:43.000Z","updated_at":"2025-10-16T17:53:10.000Z","dependencies_parsed_at":"2024-01-01T05:08:58.569Z","dependency_job_id":"3e9900ce-4ba4-4aa9-996f-25608caaae0e","html_url":"https://github.com/UofUEpiBio/epiworldR","commit_stats":{"total_commits":284,"total_committers":6,"mean_commits":"47.333333333333336","dds":0.397887323943662,"last_synced_commit":"a82f994d4f355570de2b01e963220ad48b626d7e"},"previous_names":[],"tags_count":14,"template":false,"template_full_name":null,"purl":"pkg:github/UofUEpiBio/epiworldR","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UofUEpiBio%2FepiworldR","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UofUEpiBio%2FepiworldR/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UofUEpiBio%2FepiworldR/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UofUEpiBio%2FepiworldR/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/UofUEpiBio","download_url":"https://codeload.github.com/UofUEpiBio/epiworldR/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UofUEpiBio%2FepiworldR/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":280389295,"owners_count":26322507,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-22T02:00:06.515Z","response_time":63,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["abm","agent-based-modeling","covid-19","epidemics","epidemiology","r-package","r-programming","rpack","rpkg","seir","seir-model","simulation","sir","sir-model"],"created_at":"2025-10-22T05:50:17.938Z","updated_at":"2026-01-16T10:56:14.982Z","avatar_url":"https://github.com/UofUEpiBio.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n\n\u003c!-- README.md is generated from README.qmd. Please edit that file --\u003e\n\n# epiworldR \u003cimg src=\"man/figures/logo.png\" width=\"200px\" alt=\"epiworld logo\" align=\"right\"\u003e\n\n\u003c!-- badges: start --\u003e\n\n[![ForeSITE Group](https://github.com/EpiForeSITE/software/raw/e82ed88f75e0fe5c0a1a3b38c2b94509f122019c/docs/assets/foresite-software-badge.svg)](https://github.com/EpiForeSITE)\n[![CRAN status](https://www.r-pkg.org/badges/version/epiworldR)](https://CRAN.R-project.org/package=epiworldR)\n[![R-CMD-check](https://github.com/UofUEpiBio/epiworldR/actions/workflows/r.yml/badge.svg)](https://github.com/UofUEpiBio/epiworldR/actions/workflows/r.yml)\n[![CRANlogs downloads](https://cranlogs.r-pkg.org/badges/grand-total/epiworldR)](https://cran.r-project.org/package=epiworldR)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://github.com/UofUEpiBio/epiworldR/blob/master/LICENSE.md)\n[![codecov](https://codecov.io/gh/UofUEpiBio/epiworldR/graph/badge.svg?token=ZB8FVLI7GN)](https://app.codecov.io/gh/UofUEpiBio/epiworldR)\n[![status](https://tinyverse.netlify.app/badge/epiworldR)](https://CRAN.R-project.org/package=epiworldR)\n\n\u003c!-- badges: end --\u003e\n\nThis R package is a wrapper of the C++ library\n\u003ca href=\"https://github.com/UofUEpiBio/epiworld\"\ntarget=\"_blank\"\u003eepiworld\u003c/a\u003e. It provides a general framework for\nmodeling disease transmission using \u003ca\nhref=\"https://en.wikipedia.org/w/index.php?title=Agent-based_model\u0026amp;oldid=1153634802\"\ntarget=\"_blank\"\u003eagent-based models\u003c/a\u003e. Some of the main features\ninclude:\n\n- Fast simulation with an average of 30 million agents/day per second.\n- One model can include multiple diseases.\n- Policies (tools) can be multiple and user-defined.\n- Transmission can be a function of agents’ features.\n- Out-of-the-box parallelization for multiple simulations.\n\nFrom the package’s description:\n\n\u003e A flexible framework for Agent-Based Models (ABM), the epiworldR\n\u003e package provides methods for prototyping disease outbreaks and\n\u003e transmission models using a C++ backend, making it very fast. It\n\u003e supports multiple epidemiological models, including the\n\u003e Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Removed\n\u003e (SIR), Susceptible-Exposed-Infected-Removed (SEIR), and others,\n\u003e involving arbitrary mitigation policies and multiple-disease models.\n\u003e Users can specify infectiousness/susceptibility rates as a function of\n\u003e agents’ features, providing great complexity for the model dynamics.\n\u003e Furthermore, epiworldR is ideal for simulation studies featuring large\n\u003e populations.\n\nCurrent available models:\n\n**Note:** Measles-specific models have been moved to the separate\n[`measles`](https://github.com/UofUEpiBio/measles) R package.\n\n**Note:** Measles-specific models have been moved to the separate\n[`measles`](https://github.com/UofUEpiBio/measles) R package.\n\n1.  `ModelDiagram`\n2.  `ModelDiffNet`\n3.  `ModelSEIR`\n4.  `ModelSEIRCONN`\n5.  `ModelSEIRD`\n6.  `ModelSEIRDCONN`\n7.  `ModelSEIRMixing`\n8.  `ModelSEIRMixingQuarantine`\n9.  `ModelSIR`\n10. `ModelSIRCONN`\n11. `ModelSIRD`\n12. `ModelSIRDCONN`\n13. `ModelSIRLogit`\n14. `ModelSIRMixing`\n15. `ModelSIS`\n16. `ModelSISD`\n17. `ModelSURV`\n\n## Installation\n\nYou can install the development version of epiworldR from\n[GitHub](https://github.com/) with:\n\n``` r\ndevtools::install_github(\"UofUEpiBio/epiworldR\")\n```\n\nOr from \u003ca href=\"https://uofuepibio.r-universe.dev/\"\ntarget=\"_blank\"\u003eR-universe\u003c/a\u003e (recommended for the latest development\nversion):\n\n``` r\ninstall.packages(\n  'epiworldR',\n  repos = c(\n    'https://uofuepibio.r-universe.dev',\n    'https://cloud.r-project.org'\n  )\n)\n```\n\nOr from CRAN\n\n``` r\ninstall.packages(\"epiworldR\")\n```\n\n# Examples\n\nThis R package includes several popular epidemiological models,\nincluding \u003ca\nhref=\"https://en.wikipedia.org/w/index.php?title=Compartmental_models_in_epidemiology\u0026amp;oldid=1155757336#Variations_on_the_basic_SIR_model\"\ntarget=\"_blank\"\u003eSIS\u003c/a\u003e, \u003ca\nhref=\"https://en.wikipedia.org/w/index.php?title=Compartmental_models_in_epidemiology\u0026amp;oldid=1155757336#The_SIR_model\"\ntarget=\"_blank\"\u003eSIR\u003c/a\u003e, and \u003ca\nhref=\"https://en.wikipedia.org/w/index.php?title=Compartmental_models_in_epidemiology\u0026amp;oldid=1155757336#The_SEIR_model\"\ntarget=\"_blank\"\u003eSEIR\u003c/a\u003e using either a fully connected graph (similar\nto a compartmental model) or a user-defined network.\n\n## SIR model using a random graph\n\nThis Susceptible-Infected-Recovered model features a population of\n100,000 agents simulated in a small-world network. Each agent is\nconnected to ten other agents. One percent of the population has the\nvirus, with a 70% chance of transmission. Infected individuals recover\nat a 0.3 rate:\n\n``` r\nlibrary(epiworldR)\n#\u003e Thank you for using epiworldR! Please consider citing it in your work.\n#\u003e You can find the citation information by running\n#\u003e   citation(\"epiworldR\")\n\n# Creating a SIR model\nsir \u003c- ModelSIR(\n  name              = \"COVID-19\",\n  prevalence        = .01,\n  transmission_rate = .7,\n  recovery          = .3\n) |\u003e\n  # Adding a Small world population\n  agents_smallworld(n = 100000, k = 10, d = FALSE, p = .01) |\u003e\n  # Running the model for 50 days\n  run(ndays = 50, seed = 1912)\n#\u003e _________________________________________________________________________\n#\u003e |Running the model...\n#\u003e |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.\n#\u003e |\n\nsir\n#\u003e ________________________________________________________________________________\n#\u003e Susceptible-Infected-Recovered (SIR)\n#\u003e It features 100000 agents, 1 virus(es), and 0 tool(s).\n#\u003e The model has 3 states.\n#\u003e The final distribution is: 0 Susceptible, 0 Infected, and 100000 Recovered.\n```\n\nVisualizing the outputs\n\n``` r\nsummary(sir)\n#\u003e ________________________________________________________________________________\n#\u003e ________________________________________________________________________________\n#\u003e SIMULATION STUDY\n#\u003e\n#\u003e Name of the model   : Susceptible-Infected-Recovered (SIR)\n#\u003e Population size     : 100000\n#\u003e Agents' data        : (none)\n#\u003e Number of entities  : 0\n#\u003e Days (duration)     : 50 (of 50)\n#\u003e Number of viruses   : 1\n#\u003e Last run elapsed t  : 124.00ms\n#\u003e Last run speed      : 40.28 million agents x day / second\n#\u003e Rewiring            : off\n#\u003e\n#\u003e Global events:\n#\u003e  (none)\n#\u003e\n#\u003e Virus(es):\n#\u003e  - COVID-19\n#\u003e\n#\u003e Tool(s):\n#\u003e  (none)\n#\u003e\n#\u003e Model parameters:\n#\u003e  - Recovery rate     : 0.3000\n#\u003e  - Transmission rate : 0.7000\n#\u003e\n#\u003e Distribution of the population at time 50:\n#\u003e   - (0) Susceptible :  99000 -\u003e 0\n#\u003e   - (1) Infected    :   1000 -\u003e 0\n#\u003e   - (2) Recovered   :      0 -\u003e 100000\n#\u003e\n#\u003e Transition Probabilities:\n#\u003e  - Susceptible  0.85  0.15     -\n#\u003e  - Infected        -  0.70  0.30\n#\u003e  - Recovered       -     -  1.00\nplot(sir)\n```\n\n\u003cimg src=\"man/figures/README-sir-figures-1.png\" style=\"width:100.0%\" /\u003e\n\n``` r\nplot_incidence(sir)\n```\n\n\u003cimg src=\"man/figures/README-sir-figures-2.png\" style=\"width:100.0%\" /\u003e\n\n## SEIR model with a fully connected graph\n\nThe SEIR model is similar to the SIR model but includes an exposed\nstate. Here, we simulate a population of 10,000 agents with a 0.01\nprevalence, a 0.6 transmission rate, a 0.5 recovery rate, and 7\ndays-incubation period. The population is fully connected, meaning\nagents can transmit the disease to any other agent:\n\n``` r\nmodel_seirconn \u003c- ModelSEIRCONN(\n  name                = \"COVID-19\",\n  prevalence          = 0.01,\n  n                   = 10000,\n  contact_rate        = 10,\n  incubation_days     = 7,\n  transmission_rate   = 0.1,\n  recovery_rate       = 1 / 7\n) |\u003e add_virus(\n  virus(\n    name = \"COVID-19 (delta)\",\n    prevalence = 0.01,\n    as_proportion = TRUE,\n    prob_infecting = 0.2,\n    recovery_rate = 0.6,\n    prob_death = 0.5,\n    incubation = 7\n))\n\nset.seed(132)\nrun(model_seirconn, ndays = 100)\n#\u003e _________________________________________________________________________\n#\u003e Running the model...\n#\u003e ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.\nsummary(model_seirconn)\n#\u003e ________________________________________________________________________________\n#\u003e ________________________________________________________________________________\n#\u003e SIMULATION STUDY\n#\u003e\n#\u003e Name of the model   : Susceptible-Exposed-Infected-Removed (SEIR) (connected)\n#\u003e Population size     : 10000\n#\u003e Agents' data        : (none)\n#\u003e Number of entities  : 0\n#\u003e Days (duration)     : 100 (of 100)\n#\u003e Number of viruses   : 2\n#\u003e Last run elapsed t  : 63.00ms\n#\u003e Last run speed      : 15.86 million agents x day / second\n#\u003e Rewiring            : off\n#\u003e\n#\u003e Global events:\n#\u003e  - Update infected individuals (runs daily)\n#\u003e\n#\u003e Virus(es):\n#\u003e  - COVID-19\n#\u003e  - COVID-19 (delta)\n#\u003e\n#\u003e Tool(s):\n#\u003e  (none)\n#\u003e\n#\u003e Model parameters:\n#\u003e  - Avg. Incubation days : 7.0000\n#\u003e  - Contact rate         : 10.0000\n#\u003e  - Prob. Recovery       : 0.1429\n#\u003e  - Prob. Transmission   : 0.1000\n#\u003e\n#\u003e Distribution of the population at time 100:\n#\u003e   - (0) Susceptible :  9800 -\u003e 59\n#\u003e   - (1) Exposed     :   200 -\u003e 0\n#\u003e   - (2) Infected    :     0 -\u003e 0\n#\u003e   - (3) Recovered   :     0 -\u003e 9941\n#\u003e\n#\u003e Transition Probabilities:\n#\u003e  - Susceptible  0.95  0.05     -     -\n#\u003e  - Exposed         -  0.86  0.14     -\n#\u003e  - Infected        -     -  0.79  0.21\n#\u003e  - Recovered       -     -     -  1.00\n```\n\nComputing some key statistics\n\n``` r\nplot(model_seirconn)\n```\n\n\u003cimg src=\"man/figures/README-seir-conn-figures-1.png\"\nstyle=\"width:100.0%\" /\u003e\n\n``` r\n\nrepnum \u003c- get_reproductive_number(model_seirconn)\n\nhead(plot(repnum))\n```\n\n\u003cimg src=\"man/figures/README-seir-conn-figures-2.png\"\nstyle=\"width:100.0%\" /\u003e\n\n    #\u003e   virus_id    virus date      avg   n       sd lb    ub\n    #\u003e 1        0 COVID-19    0 4.880000 100 4.159157  0 14.05\n    #\u003e 2        0 COVID-19    2 5.333333   9 4.415880  1 12.60\n    #\u003e 3        0 COVID-19    3 5.416667  12 3.369875  1 10.45\n    #\u003e 4        0 COVID-19    4 3.480000  25 2.740438  0  9.80\n    #\u003e 5        0 COVID-19    5 2.580645  31 2.790152  0  9.00\n    #\u003e 6        0 COVID-19    6 3.339623  53 3.031523  0 10.40\n\n    head(plot_generation_time(model_seirconn))\n\n\u003cimg src=\"man/figures/README-seir-conn-figures-3.png\"\nstyle=\"width:100.0%\" /\u003e\n\n    #\u003e   date      avg  n       sd ci_lower ci_upper    virus virus_id\n    #\u003e 1    0 8.318681 91 5.599363     2.00   20.500 COVID-19        0\n    #\u003e 2    2 6.888889  9 4.648775     2.20   14.000 COVID-19        0\n    #\u003e 3    3 5.083333 12 2.503028     3.00    9.725 COVID-19        0\n    #\u003e 4    4 6.095238 21 3.096849     2.00   12.500 COVID-19        0\n    #\u003e 5    5 8.173913 23 6.846712     2.55   24.000 COVID-19        0\n    #\u003e 6    6 7.272727 44 5.332496     2.00   19.000 COVID-19        0\n\n## SIR Logit\n\nThis model provides a more complex transmission and recovery pattern\nbased on agents’ features. With it, we can reflect co-morbidities that\ncould change the probability of infection and recovery. Here, we\nsimulate a population including a dataset with two features: an\nintercept and a binary variable `Female`. The probability of infection\nand recovery are functions of the intercept and the `Female` variables.\nThe following code simulates a population of 100,000 agents in a\nsmall-world network. Each agent is connected to eight other agents. One\npercent of the population has the virus, with an 80% chance of\ntransmission. Infected individuals recover at a 0.3 rate:\n\n``` r\n# Simulating a population of 100,000 agents\nset.seed(2223)\nn \u003c- 100000\n\n# Agents' features\nX \u003c- cbind(\n  Intercept = 1,\n  Female    = sample.int(2, n, replace = TRUE) - 1\n)\n\ncoef_infect  \u003c- c(.1, -2, 2)\ncoef_recover \u003c- rnorm(2)\n\n# Creating the model\nmodel_logit \u003c- ModelSIRLogit(\n  \"covid2\",\n  data = X,\n  coefs_infect      = coef_infect,\n  coefs_recover     = coef_recover,\n  coef_infect_cols  = 1L:ncol(X),\n  coef_recover_cols = 1L:ncol(X),\n  prob_infection = .8,\n  recovery_rate = .3,\n  prevalence = .01\n)\n\n# Adding a small-world population\nagents_smallworld(model_logit, n, 8, FALSE, .01)\n\n# Running the model\nrun(model_logit, 50)\n#\u003e _________________________________________________________________________\n#\u003e |Running the model...\n#\u003e |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.\n#\u003e |\nplot(model_logit)\n```\n\n\u003cimg src=\"man/figures/README-logit-model-1.png\" style=\"width:100.0%\" /\u003e\n\n``` r\n\n# Females are supposed to be more likely to become infected\nrn \u003c- get_reproductive_number(model_logit)\n\n(table(\n  X[, \"Female\"],\n  (1:n %in% rn$source)\n) |\u003e prop.table())[, 2]\n#\u003e       0       1\n#\u003e 0.20717 0.22397\n\n# Looking into the agents\nget_agents(model_logit)\n#\u003e Agents from the model \"Susceptible-Infected-Removed (SIR) (logit)\":\n#\u003e Agent: 0, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8\n#\u003e Agent: 1, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8\n#\u003e Agent: 2, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8\n#\u003e Agent: 3, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8\n#\u003e Agent: 4, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8\n#\u003e Agent: 5, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8\n#\u003e Agent: 6, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8\n#\u003e Agent: 7, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8\n#\u003e Agent: 8, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8\n#\u003e Agent: 9, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8\n#\u003e ... 99990 more agents ...\n```\n\n## Transmission network\n\nThis example shows how we can draw a transmission network from a\nsimulation. The following code simulates a population of 500 agents in a\nsmall-world network. Each agent is connected to ten other agents. One\npercent of the population has the virus, with a 50% chance of\ntransmission. Infected individuals recover at a 0.5 rate:\n\n``` r\n# Creating a SIR model\nsir \u003c- ModelSIR(\n  name           = \"COVID-19\",\n  prevalence     = .01,\n  transmission_rate = .5,\n  recovery       = .5\n) |\u003e\n  # Adding a Small world population\n  agents_smallworld(n = 500, k = 10, d = FALSE, p = .01) |\u003e\n  # Running the model for 50 days\n  run(ndays = 50, seed = 1912)\n#\u003e _________________________________________________________________________\n#\u003e |Running the model...\n#\u003e |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.\n#\u003e |\n\n# Transmission network\nnet \u003c- get_transmissions(sir)\nnet \u003c- subset(net, source \u003e= 0)\n\n# Plotting\nlibrary(epiworldR)\nlibrary(netplot)\n#\u003e Loading required package: grid\nx \u003c- igraph::graph_from_edgelist(\n  as.matrix(net[, 2:3]) + 1\n)\n\nnplot(x, edge.curvature = 0, edge.color = \"gray\", skip.vertex = TRUE)\n```\n\n\u003cimg src=\"man/figures/README-transmission-net-1.png\"\nstyle=\"width:100.0%\" /\u003e\n\n## Multiple simulations\n\n`epiworldR` supports running multiple simulations using the\n`run_multiple` function. The following code simulates 50 SIR models with\n1000 agents each. Each agent is connected to ten other agents. One\npercent of the population has the virus, with a 90% chance of\ntransmission. Infected individuals recover at a 0.1 rate. The results\nare saved in a `data.frame`:\n\n``` r\nmodel_sir \u003c- ModelSIRCONN(\n  name = \"COVID-19\",\n  prevalence = 0.01,\n  n = 1000,\n  contact_rate = 2,\n  transmission_rate = 0.9, recovery_rate = 0.1\n)\n\n# Generating a saver\nsaver \u003c- make_saver(\"total_hist\", \"reproductive\")\n\n# Running and printing\n# Notice the use of nthread = 2 to run the simulations in parallel\nrun_multiple(model_sir, ndays = 100, nsims = 50, saver = saver, nthread = 2)\n#\u003e Starting multiple runs (50) using 2 thread(s)\n#\u003e _________________________________________________________________________\n#\u003e _________________________________________________________________________\n#\u003e ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.\n\n# Retrieving the results\nans \u003c- run_multiple_get_results(model_sir)\n\nhead(ans$total_hist)\n#\u003e   sim_num date nviruses       state counts\n#\u003e 1       1    0        1 Susceptible    990\n#\u003e 2       1    0        1    Infected     10\n#\u003e 3       1    0        1   Recovered      0\n#\u003e 4       1    1        1 Susceptible    973\n#\u003e 5       1    1        1    Infected     27\n#\u003e 6       1    1        1   Recovered      0\nhead(ans$reproductive)\n#\u003e   sim_num virus_id    virus source source_exposure_date rt\n#\u003e 1       1        0 COVID-19    683                    9  0\n#\u003e 2       1        0 COVID-19    983                    8  0\n#\u003e 3       1        0 COVID-19    875                    8  0\n#\u003e 4       1        0 COVID-19    801                    8  0\n#\u003e 5       1        0 COVID-19    770                    8  0\n#\u003e 6       1        0 COVID-19    758                    8  0\n\nplot(ans$reproductive)\n```\n\n\u003cimg src=\"man/figures/README-multiple-example-1.png\"\nstyle=\"width:100.0%\" /\u003e\n\n# Tutorials\n\n- The virtual INSNA Sunbelt 2023 session can be found here:\n  https://github.com/UofUEpiBio/epiworldR-workshop/tree/sunbelt2023-virtual\n\n- The in-person INSNA Sunbelt 2023 session can be found here:\n  https://github.com/UofUEpiBio/epiworldR-workshop/tree/sunbetl2023-inperson\n\n# Citation\n\nIf you use `epiworldR` in your research, please cite it as follows:\n\n``` r\ncitation(\"epiworldR\")\n#\u003e To cite epiworldR in publications use:\n#\u003e\n#\u003e   Meyer, Derek and Vega Yon, George (2023). epiworldR: Fast Agent-Based\n#\u003e   Epi Models. Journal of Open Source Software, 8(90), 5781,\n#\u003e   https://doi.org/10.21105/joss.05781\n#\u003e\n#\u003e And the actual R package:\n#\u003e\n#\u003e   Meyer D, Pulsipher A, Vega Yon G (2025). _epiworldR: Fast Agent-Based\n#\u003e   Epi Models_. R package version 0.11.0.1,\n#\u003e   \u003chttps://github.com/UofUEpiBio/epiworldR\u003e.\n#\u003e\n#\u003e To see these entries in BibTeX format, use 'print(\u003ccitation\u003e,\n#\u003e bibtex=TRUE)', 'toBibtex(.)', or set\n#\u003e 'options(citation.bibtex.max=999)'.\n```\n\n# Existing Alternatives\n\nSeveral alternatives to `epiworldR` exist and provide researchers with a\nrange of options, each with its own unique features and strengths,\nenabling the exploration and analysis of infectious disease dynamics\nthrough agent-based modeling. Below is a manually curated table of\nexisting alternatives, including ABM \\[@ABM\\], abmR \\[@abmR\\], cystiSim\n\\[@cystiSim\\], villager \\[@villager\\], and RNetLogo \\[@RNetLogo\\].\n\n| Package                                                                     | Multiple Viruses | Multiple Tools | Multiple Runs | Global Actions | Built-In Epi Models |  Dependencies                                                                                             | Activity                                                                                                               |\n|:--------|:--------|:--------|:--------|:--------|---------|:--------|:--------|\n| [**epiworldR**](https://cran.r-project.org/package=epiworldR)               | yes              | yes            | yes           | yes            | yes                 | [![status](https://tinyverse.netlify.app/badge//epiworldR)](https://CRAN.R-project.org/package=epiworldR) | [![Activity](https://img.shields.io/github/last-commit/UofUEpiBio/epiworldR)](https://github.com/UofUEpiBio/epiworldR) |\n| [**ABM**](https://cran.r-project.org/package=ABM)                           | \\-               | \\-             | \\-            | yes            | yes                 | [![status](https://tinyverse.netlify.app/badge//ABM)](https://CRAN.R-project.org/package=ABM)             | [![Activity](https://img.shields.io/github/last-commit/junlingm/ABM)](https://github.com/junlingm/ABM)                 |\n| [**abmR**](https://cran.r-project.org/package=abmR)                         | \\-               | \\-             | yes           | \\-             | \\-                  | [![status](https://tinyverse.netlify.app/badge/abmR)](https://CRAN.R-project.org/package=abmR)           | [![Activity](https://img.shields.io/github/last-commit/bgoch5/abmR)](https://github.com/bgoch5/abmR)                   |\n| [**cystiSim**](https://cran.r-project.org/package=cystiSim)                 | \\-               | yes            | yes           | \\-             | \\-                  | [![status](https://tinyverse.netlify.app/badge/cystiSim)](https://CRAN.R-project.org/package=cystiSim)   | [![Activity](https://img.shields.io/github/last-commit/brechtdv/cystiSim)](https://github.com/brechtdv/cystiSim)       |\n| [**villager**](https://cran.r-project.org/package=villager)                 | \\-               | \\-             | \\-            | yes            | \\-                  | [![status](https://tinyverse.netlify.app/badge/villager)](https://CRAN.R-project.org/package=villager)   | [![Activity](https://img.shields.io/github/last-commit/zizroc/villager)](https://github.com/zizroc/villager)           |\n| [**RNetLogo**](https://cran.r-project.org/package=RNetLogo) | \\-               | yes            | yes           | yes            | \\-                  | [![status](https://tinyverse.netlify.app/badge/RNetLogo)](https://CRAN.R-project.org/package=RNetLogo)   | [![Activity](https://img.shields.io/github/last-commit/cran/RNetLogo)](https://github.com/cran/RNetLogo)               |\n\n# Other ABM R packages\n\nYou may want to check out other R packages for agent-based modeling:\n\u003ca href=\"https://cran.r-project.org/package=ABM\"\ntarget=\"_blank\"\u003e\u003ccode\u003eABM\u003c/code\u003e\u003c/a\u003e,\n\u003ca href=\"https://cran.r-project.org/package=abmR\"\ntarget=\"_blank\"\u003e\u003ccode\u003eabmR\u003c/code\u003e\u003c/a\u003e,\n\u003ca href=\"https://cran.r-project.org/package=cystiSim\"\ntarget=\"_blank\"\u003e\u003ccode\u003ecystiSim\u003c/code\u003e\u003c/a\u003e,\n\u003ca href=\"https://cran.r-project.org/package=villager\"\ntarget=\"_blank\"\u003e\u003ccode\u003evillager\u003c/code\u003e\u003c/a\u003e, and\n\u003ca href=\"https://cran.r-project.org/package=RNetLogo\"\ntarget=\"_blank\"\u003e\u003ccode\u003eRNetLogo\u003c/code\u003e\u003c/a\u003e.\n\n# Contributing to epiworldR\n\nWe welcome contributions to the epiworldR package! If you would like to\ncontribute, please review our [development\nguidelines](https://github.com/UofUEpiBio/epiworldR/blob/main/DEVELOPMENT.md)\nbefore creating a pull request.\n\n## Code of Conduct\n\nThe epiworldR project is released with a [Contributor Code of\nConduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html).\nBy contributing to this project, you agree to abide by its terms.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fuofuepibio%2Fepiworldr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fuofuepibio%2Fepiworldr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fuofuepibio%2Fepiworldr/lists"}