Describe model properties — monty_model

Describe properties of a model. Use of this function is optional, but you can pass the return value of this as the properties argument of monty_model to enforce that your model does actually have these properties.

Usage

monty_model_properties(
  has_gradient = NULL,
  has_direct_sample = NULL,
  is_stochastic = NULL,
  has_parameter_groups = NULL,
  has_observer = NULL,
  allow_multiple_parameters = NULL
)

Arguments

has_gradient: Logical, indicating if the model has a gradient method. Use NULL (the default) to detect this from the model.
has_direct_sample: Logical, indicating if the model has a direct_sample method. Use NULL (the default) to detect this from the model.
is_stochastic: Logical, indicating if the model is stochastic. Stochastic models must supply set_rng_state and get_rng_state methods.
has_parameter_groups: Logical, indicating that the model can be decomposed into parameter groups which are independent of each other. This is indicated by using the parameter_groups field within the model object passed to monty_model, and by the presence of a by_group argument to density and (later we may also support this in gradient). Use NULL (the default) to detect this from the model.
has_observer: Logical, indicating if the model has an "observation" function, which we will describe more fully soon. An observer is a function observe which takes no arguments and returns arbitrary data about the previously evaluated density. Use NULL (the default) to detect this from the model.
allow_multiple_parameters: Logical, indicating if the density calculation can support being passed a matrix of parameters (with each column corresponding to a different parameter set) and return a vector of densities. If FALSE, we will support some different approaches to sort this out for you if this feature is needed. This cannot be detected from the model, and the default is FALSE because it's not always straightforward to implement. However, where it is possible it may be much more efficient (via vectorisation or parallelisation) to do this yourself.

Value

A list of class monty_model_properties which should not be modified.

Examples

# Default properties:
monty_model_properties()
#> 
#> ── <monty_model_properties> ────────────────────────────────────────────────────
#> ℹ Unset: 'has_gradient', 'has_direct_sample', 'is_stochastic', 'has_parameter_groups', 'has_observer', and 'allow_multiple_parameters'

# Set some properties:
monty_model_properties(has_gradient = TRUE, is_stochastic = FALSE)
#> 
#> ── <monty_model_properties> ────────────────────────────────────────────────────
#> • has_gradient: `TRUE`
#> • is_stochastic: `FALSE`
#> ℹ Unset: 'has_direct_sample', 'has_parameter_groups', 'has_observer', and 'allow_multiple_parameters'