Export a stock-and-flow model

Export a model of class stockflow to another format.

Usage

export_model(
  object,
  format = c("sdbuildR", "deSolve", "psychomodels"),
  file = NULL,
  title = object[["meta"]][["name"]],
  description = object[["meta"]][["caption"]],
  explanation = description,
  publication_doi = "",
  publication_citation = "",
  framework = "Ordinary Differential Equations",
  programming_language = "R",
  psychology_discipline = "",
  software_package = "",
  model_variable = "",
  code_repository_url = "",
  data_url = "",
  submission_remarks = "",
  created_by = "",
  updated_by = "",
  published_by = "",
  published_at = Sys.time(),
  published_pending_moderation_at = Sys.time(),
  publication_citation_fetched_at = Sys.time(),
  publication_csl_fetched_at = Sys.time(),
  publication_csl_json = "",
  id = NA,
  slug = NULL,
  include_latex = TRUE,
  pretty = TRUE
)

Arguments

object

Stock-and-flow model, object of class stockflow.

format

Export format. One of "sdbuildR", "deSolve", or "psychomodels".

file

Output file path, or NULL to return the result directly.

title

[psychomodels] Model title. Defaults to object[["meta"]][["name"]].

description

[psychomodels] Model description. Defaults to object[["meta"]][["caption"]].

explanation

[psychomodels] Free-text explanation. Defaults to description.

publication_doi

[psychomodels] DOI for the associated publication.

publication_citation

[psychomodels] Citation text.

framework

[psychomodels] Modeling framework. Defaults to "Ordinary Differential Equations".

programming_language

[psychomodels] Programming language.

psychology_discipline

[psychomodels] Discipline id(s), comma-separated.

software_package

[psychomodels] Package id(s), comma-separated.

model_variable

[psychomodels] Variable id(s), comma-separated.

code_repository_url

[psychomodels] URL to code repository.

data_url

[psychomodels] URL to model data.

submission_remarks

[psychomodels] Optional remarks.

created_by

[psychomodels] Identifier of creating user.

updated_by

[psychomodels] Identifier of last updating user.

published_by

[psychomodels] Identifier of publishing user.

published_at

[psychomodels] Publication timestamp. Defaults to current time.

published_pending_moderation_at

[psychomodels] Moderation timestamp.

publication_citation_fetched_at

[psychomodels] Citation fetch timestamp.

publication_csl_fetched_at

[psychomodels] CSL fetch timestamp.

publication_csl_json

[psychomodels] CSL JSON text.

id

[psychomodels] Optional record id.

slug

[psychomodels] Optional slug. Generated from title if NULL.

include_latex

[psychomodels] If TRUE, append LaTeX equations to explanation.

pretty

[psychomodels] If TRUE, pretty-print output JSON.

Value

For file = NULL: a character string containing the exported content. For file specified: invisibly returns the file path.

Details

sdbuildR format (format = "sdbuildR")

Returns R code that reconstructs the model using sdbuildR functions. When file = NULL, returns a character string. When file is provided, writes an .R file and returns the path invisibly. If file has no .R extension, one is appended.

deSolve format (format = "deSolve")

Returns a standalone R script using deSolve::ode() directly — no sdbuildR dependency required to run the output. When file = NULL, returns a character string. When file is provided, writes an .R file and returns the path invisibly. If file has no .R extension, one is appended. Requires sim_settings(language = "R") (the default).

Psychomodels format (format = "psychomodels")

Generates a JSON record for upload to Psychomodels. When file = NULL, returns a JSON character string. When file is provided, writes a .json file and returns the path invisibly. If file has no .json extension, one is appended.

See also

import_insightmaker(), import_desolve()

Examples

sfm <- stockflow("SIR")

# Get sdbuildR reconstruction code
cat(export_model(sfm, format = "sdbuildR"))
#> sfm <-	stockflow() |>
#> 	sim_settings(start = "0.0", stop = "20.0", dt = "0.01", time_units = "weeks") |>
#> 	meta(name = "Susceptible-Infected-Recovered (SIR)", created = "2026-06-15 12:02:56.208897") |>
#> 	stock(infected, eqn = 1, label = "Infected") |>
#> 	stock(recovered, eqn = 0, label = "Recovered") |>
#> 	stock(susceptible, eqn = 99999, label = "Susceptible") |>
#> 	flow(new_infections, eqn = infection_rate * susceptible * infected, to = infected, from = susceptible, label = "New infections") |>
#> 	flow(new_recoveries, eqn = recovery_rate * infected, to = recovered, from = infected, label = "New recoveries") |>
#> 	constant(contact_rate, eqn = 2, label = "Contact rate") |>
#> 	constant(infection_rate, eqn = contact_rate / total_population, label = "Infection rate") |>
#> 	constant(recovery_rate, eqn = 0.1, label = "Recovery rate") |>
#> 	constant(total_population, eqn = susceptible + infected + recovered, label = "Total population")

# Get standalone deSolve script
cat(export_model(sfm, format = "deSolve"))
#> library(deSolve)
#> 
#> 
#> # Define time sequence
#> dt = 0.01
#> times <- seq(from=0.0, to=20.0, by=dt)
#> t = times[1]
#> 
#> 
#> 
#> 
#> 
#> 
#> # Define ODE
#> ode_func = function(t, current_state, constants){
#> 
#>     current_state = as.list(current_state)
#> 
#>     # Compute change in stocks at current time t
#>     with(c(current_state, constants), {
#> 
#>         # Update auxiliaries and flows
#>         new_infections <- infection_rate * susceptible * infected # Flow from susceptible to infected
#> 		new_recoveries <- recovery_rate * infected # Flow from infected to recovered
#> 
#>         # Collect inflows and outflows for each stock
#>         dinfected <- new_infections - new_recoveries
#> 		drecovered <- new_recoveries
#> 		dsusceptible <-  - new_infections
#> 
#>         # Combine change in stocks
#>         dSdt = c(dinfected, drecovered, dsusceptible)
#> 
#>         return(list(dSdt))
#>       })
#>       }
#> 
#> 
#> # Define parameters, initial conditions, and functions in correct order
#> 
#> contact_rate = 2
#> recovery_rate = 0.1
#> susceptible = 99999
#> infected = 1
#> recovered = 0
#> total_population = susceptible + infected + recovered
#> infection_rate = contact_rate / total_population
#> 
#> # Define parameters in named list
#> constants = list(contact_rate = contact_rate, infection_rate = infection_rate, recovery_rate = recovery_rate, total_population = total_population)
#> 
#> 
#> # Define initial condition
#> init = c(infected = infected, recovered = recovered, susceptible = susceptible)
#> 
#> 
#> # Run ODE
#> df = as.data.frame(deSolve::ode(
#>   func=ode_func,
#>   y=init,
#>   times=times,
#>   parms=constants,
#>   method = 'euler'
#> )) 

# Export to Psychomodels JSON
if (FALSE) { # \dontrun{
json <- export_model(sfm,
  format = "psychomodels",
  publication_doi = "10.0000/example"
)
} # }