Types

Result types for brmspy functions.

Classes

PriorSpec dataclass

Python representation of a brms prior specification.

This dataclass provides a typed interface to brms::prior_string() arguments, allowing Python developers to specify priors with IDE autocomplete and type checking. Use the prior() factory function to create instances.

Attributes:

Name	Type	Description
prior	str	Prior distribution as string (e.g., "normal(0, 1)", "exponential(2)")
class_	(str, optional)	Parameter class: "b" (fixed effects), "sd" (group SD), "Intercept", "sigma", "cor", etc.
coef	(str, optional)	Specific coefficient name for class-level priors
group	(str, optional)	Grouping variable for hierarchical effects
dpar	(str, optional)	Distributional parameter (e.g., "sigma", "phi", "zi")
resp	(str, optional)	Response variable for multivariate models
nlpar	(str, optional)	Non-linear parameter name
lb	(float, optional)	Lower bound for truncated priors
ub	(float, optional)	Upper bound for truncated priors

See Also

prior : Factory function to create PriorSpec instances brms::prior_string : R documentation https://paulbuerkner.com/brms/reference/prior_string.html

Examples:

Create prior specifications (prefer using prior() function):

from brmspy.types import PriorSpec

# Fixed effect prior
p1 = PriorSpec(prior="normal(0, 1)", class_="b")

# Group-level SD prior
p2 = PriorSpec(
    prior="exponential(2)",
    class_="sd",
    group="patient"
)

# Coefficient-specific prior with bounds
p3 = PriorSpec(
    prior="normal(0, 1)",
    class_="b",
    coef="age",
    lb=0  # Truncated at zero
)

Functions

to_brms_kwargs()

Convert PriorSpec to keyword arguments for brms::prior_string().

Maps Python dataclass fields to R function arguments, handling the class_ -> class parameter name conversion.

Returns:

Type	Description
dict	Keyword arguments ready for brms::prior_string()

Examples:

from brmspy import prior
p = prior("normal(0, 1)", class_="b", coef="age")
kwargs = p.to_brms_kwargs()
print(kwargs)
# {'prior': 'normal(0, 1)', 'class': 'b', 'coef': 'age'}

IDFit

Bases: InferenceData

Typed InferenceData for fitted brms models.

Extends arviz.InferenceData with type hints for IDE autocomplete. Guarantees the presence of specific data groups returned by fit().

Attributes:

Name	Type	Description
posterior	Dataset	Posterior samples of model parameters
posterior_predictive	Dataset	Posterior predictive samples (with observation noise)
log_likelihood	Dataset	Log-likelihood values for each observation
observed_data	Dataset	Original observed response data
coords	dict	Coordinate mappings for dimensions
dims	dict	Dimension specifications for variables

See Also

brmspy.brms.fit : Creates IDFit objects arviz.InferenceData : Base class documentation

Examples:

from brmspy import brms
model = brms.fit("y ~ x", data=df, chains=4)

# Type checking and autocomplete
assert isinstance(model.idata, IDFit)
print(model.idata.posterior)  # IDE autocomplete works!

IDEpred

Bases: InferenceData

Typed InferenceData for posterior_epred results.

Contains expected values E[Y|X] without observation noise.

Attributes:

Name	Type	Description
posterior	Dataset	Expected value samples (no observation noise)

See Also

brmspy.brms.posterior_epred : Creates IDEpred objects

IDPredict

Bases: InferenceData

Typed InferenceData for posterior_predict results.

Contains posterior predictive samples with observation noise.

Attributes:

Name	Type	Description
posterior_predictive	Dataset	Posterior predictive samples (includes observation noise)

See Also

brmspy.brms.posterior_predict : Creates IDPredict objects

IDLinpred

Bases: InferenceData

Typed InferenceData for posterior_linpred results.

Contains linear predictor values (before applying link function).

Attributes:

Name	Type	Description
predictions	Dataset	Linear predictor samples

See Also

brmspy.brms.posterior_linpred : Creates IDLinpred objects

IDLogLik

Bases: InferenceData

Typed InferenceData for log_lik results.

Contains log-likelihood values for model comparison.

Attributes:

Name	Type	Description
log_likelihood	Dataset	Log-likelihood values for each observation

See Also

brmspy.brms.log_lik : Creates IDLogLik objects arviz.loo : LOO-CV using log-likelihood

RListVectorExtension dataclass

Generic result container with R objects.

Attributes:

Name	Type	Description
r	ListVector	R object from brms

GenericResult dataclass

Bases: RListVectorExtension

Generic result container with arviz and R objects.

Attributes:

Name	Type	Description
idata	InferenceData	arviz InferenceData object
r	ListVector	R object from brms

FitResult dataclass

Bases: RListVectorExtension

Result from fit() function.

Attributes:

Name	Type	Description
idata	InferenceData	arviz InferenceData with posterior, posterior_predictive, log_likelihood, and observed_data groups
r	ListVector	brmsfit R object from brms::brm()

PosteriorEpredResult dataclass

Bases: RListVectorExtension

Result from posterior_epred() function.

Attributes:

Name	Type	Description
idata	InferenceData	arviz InferenceData with expected values in 'posterior' group
r	ListVector	R matrix from brms::posterior_epred()

PosteriorPredictResult dataclass

Bases: RListVectorExtension

Result from posterior_predict() function.

Attributes:

Name	Type	Description
idata	InferenceData	arviz InferenceData with predictions in 'posterior_predictive' group
r	ListVector	R matrix from brms::posterior_predict()

LogLikResult dataclass

Bases: RListVectorExtension

Result from log_lik() function.

Attributes:

Name	Type	Description
idata	IDLogLik	arviz InferenceData with log-likelihood values
r	ListVector	R matrix from brms::log_lik()

See Also

brmspy.brms.log_lik : Creates LogLikResult objects

Examples:

from brmspy import brms
import arviz as az

model = brms.fit("y ~ x", data=df, chains=4)
loglik = brms.log_lik(model)

# Use for model comparison
loo = az.loo(model.idata)
print(loo)

PosteriorLinpredResult dataclass

Bases: RListVectorExtension

Result from posterior_linpred() function.

Attributes:

Name	Type	Description
idata	IDLinpred	arviz InferenceData with linear predictor values
r	ListVector	R matrix from brms::posterior_linpred()

See Also

brmspy.brms.posterior_linpred : Creates PosteriorLinpredResult objects

Examples:

from brmspy import brms

model = brms.fit("count ~ age", data=df, family="poisson", chains=4)
linpred = brms.posterior_linpred(model)

# Linear predictor on log scale (for Poisson)
print(linpred.idata.predictions)

FormulaResult dataclass

Bases: RListVectorExtension

Result from formula() function.

Attributes:

Name	Type	Description
r	ListVector	R brmsformula object
dict	Dict	Python dictionary representation of formula

See Also

brmspy.brms.formula : Creates FormulaResult objects

Examples:

from brmspy import brms

# Create formula with options
f = brms.formula("y ~ x", decomp="QR")

# Use in fit()
model = brms.fit(f, data=df, chains=4)

LooResult dataclass

Bases: RListVectorExtension

Functions

__repr__()

Pretty print LOO-CV results.