`generate_gold_standard()`

runs simulations in order to determine the gold standard
of the simulations.
`gold_standard_default()`

is used to configure parameters pertaining this process.

```
generate_gold_standard(model)
gold_standard_default(
tau = 30/3600,
census_interval = 10/60,
simulate_targets = FALSE
)
```

- model
A dyngen intermediary model for which the kinetics of the feature network has been generated with

`generate_kinetics()`

.- tau
The time step of the ODE algorithm used to generate the gold standard.

- census_interval
A granularity parameter of the gold standard time steps. Should be larger than or equal to

`tau`

.- simulate_targets
Also simulate the targets during the gold standard simulation

A dyngen model.

dyngen on how to run a complete dyngen simulation

```
model <-
initialise_model(
backbone = backbone_bifurcating(),
gold_standard = gold_standard_default(tau = .01, census_interval = 1)
)
# \donttest{
data("example_model")
model <- example_model %>% generate_gold_standard()
#>
plot_gold_simulations(model)
plot_gold_mappings(model)
plot_gold_expression(model)
#> geom_path: Each group consists of only one observation. Do you need to adjust
#> the group aesthetic?
# }
```