emmeans-bmmfit: emmeans support for bmmfit objects

In bmm: Easy and Accessible Bayesian Measurement Models Using 'brms'

emmeans support for bmmfit objects

Description

These S3 methods allow emmeans::emmeans() to work seamlessly with bmmfit objects by automatically resolving whether a parameter is a distributional (dpar) or non-linear (nlpar) parameter in brms. Users can always use dpar = "parameter_name" regardless of internal classification.

Usage

## S3 method for class 'bmmfit'
recover_data(object, ..., dpar = NULL, nlpar = NULL)

## S3 method for class 'bmmfit'
emm_basis(object, trms, xlev, grid, ..., dpar = NULL, nlpar = NULL)

Arguments

object	A bmmfit object (created by bmm())
...	Additional arguments passed to the brmsfit methods.
dpar	Character string. Name of the model parameter (e.g., "kappa", "c", "thetat"). Automatically resolved to the correct brms parameter type.
nlpar	Character string. Explicit non-linear parameter name. If provided, takes precedence over dpar.
trms, xlev, grid	Arguments passed by emmeans internally.

Details

bmm models use two types of parameters internally in brms:

• Distributional parameters (dpar): Used in models with custom_family(dpars = ...) (e.g., SDM, EZDM)

• Non-linear parameters (nlpar): Used in models with bmf2bf() + nlf() (e.g., mixture2p, mixture3p, IMM, M3)

Users should not need to know this distinction. These methods intercept the dpar argument and, if the parameter is actually an nlpar, silently re-route it to nlpar.

Value

See emmeans::recover_data() and emmeans::emm_basis() for return values.

See Also

emmeans::emmeans()

Examples

## Not run: 
# Works for any bmm model — no need to know dpar vs nlpar
em <- emmeans(fit, ~ condition, dpar = "kappa")
pairs(em)
confint(em)

## End(Not run)

bmm documentation built on March 30, 2026, 5:08 p.m.