prior

Prior specification helpers.

This module provides helpers for constructing brms-compatible prior specifications and for querying the default priors implied by a model.

Notes

Executed inside the worker process that hosts the embedded R session.

Classes

Functions

prior(prior, class_=None, coef=None, group=None, dpar=None, resp=None, nlpar=None, lb=None, ub=None, **kwargs)

Create a brms-style prior specification.

This function mirrors the behavior of brms::prior_string() and allows specifying priors for regression parameters, group-level effects, nonlinear parameters, distributional parameters, and more — using a typed Python interface. All arguments correspond directly to the parameters of prior_string() in brms.

Parameters:

Name	Type	Description	Default
prior	str	The prior definition as a string, exactly as brms expects it. Examples include :: "normal(0, 1)" "student_t(3, 0, 1.5)" "exponential(2)" "lkj(2)"	required
class_	str	Parameter class (e.g. "b", "sd", "Intercept"). This corresponds to class in brms. class cannot be used as a parameter in Python (reserved keyword), so class_ is used instead.	None
coef	str	Coefficient name for class-level effects.	None
group	str	Grouping variable for hierarchical/multilevel effects.	None
dpar	str	Distributional parameter (e.g. "sigma" or "phi").	None
resp	str	Response variable name for multivariate models.	None
nlpar	str	Nonlinear parameter name if using nonlinear formulas.	None
lb	float	Lower bound for truncated priors.	None
ub	float	Upper bound for truncated priors.	None
**kwargs	Any	Any additional keyword arguments supported by brms::prior_string(). These are forwarded unchanged.	{}

Returns:

Type	Description
PriorSpec	A typed prior specification object used by brmspy.brms.brm() and brmspy.brms.make_stancode().

See Also

brms::prior_string : R documentation

Notes

This function does not validate the prior expression string itself — validation occurs inside brms.

Examples:

from brmspy.brms import prior

p_intercept = prior("student_t(3, 0, 1.95)", class_="Intercept")
p_slope = prior("normal(0, 1)", class_="b", coef="age")
p_sd = prior("exponential(2)", class_="sd", group="region")
p_trunc = prior("normal(0, 1)", class_="b", coef="income", lb=0)

get_prior(formula, data=None, family='gaussian', **kwargs)

Get default priors for a model specification.

Wrapper around R brms::get_prior().

Returns a DataFrame with default priors for each parameter class in the specified brms model. Useful for reviewing and customizing priors before fitting.

Parameters:

Name	Type	Description	Default
formula	str or FormulaConstruct	Model formula (e.g. "y ~ x + (1|group)") or a composed formula.	required
data	DataFrame or dict	Dataset containing model variables. Required for data-dependent priors	None
family	str or ListSexpVector	Distribution family (e.g., "gaussian", "poisson", "binomial")	"gaussian"
**kwargs		Additional arguments passed to brms::get_prior() (e.g., autocor, data2, knots, drop_unused_levels)	{}

Returns:

Type	Description
DataFrame	DataFrame with columns: prior, class, coef, group, resp, dpar, nlpar, lb, ub, source. Each row represents a parameter or parameter class that can have a custom prior.

See Also

default_prior : Generic function for getting default priors prior : Create custom prior specifications brms::get_prior : R documentation

Examples:

from brmspy import brms
from brmspy.brms import prior

priors_df = brms.get_prior("y ~ x", data=df)

custom_priors = [
    prior("normal(0, 0.5)", class_="b"),
    prior("exponential(2)", class_="sigma"),
]

fit = brms.brm("y ~ x", data=df, priors=custom_priors, chains=4)

default_prior(object, data=None, family='gaussian', **kwargs)

Get default priors for brms model parameters (generic function).

Wrapper around R brms::default_prior().

Generic function to retrieve default prior specifications for all parameters in a brms model. Accepts formula objects, brmsformula objects, or other model specification objects. This is the generic version of get_prior().

Parameters:

Name	Type	Description	Default
object	str, FormulaResult, or ListSexpVector	Model specification: formula string, brmsformula object, mvbrmsformula, or any object that can be coerced to these classes	required
data	DataFrame or dict	Dataset containing model variables. Required for data-dependent priors	None
family	str or ListSexpVector	Distribution family (e.g., "gaussian", "poisson", "binomial"). Can be a list of families for multivariate models	"gaussian"
**kwargs		Additional arguments passed to brms::get_prior() (e.g., autocor, data2, knots, drop_unused_levels, sparse)	{}

Returns:

Type	Description
DataFrame	DataFrame with columns: prior, class, coef, group, resp, dpar, nlpar, lb, ub, source. Each row specifies a parameter class with its default prior. The 'prior' column is empty except for internal defaults.

See Also

get_prior : Convenience function with formula parameter prior : Create custom prior specifications brms::default_prior : R documentation

Examples:

Get default priors for a Poisson model:

from brmspy import brms

priors = brms.default_prior(
    object="count ~ zAge + zBase * Trt + (1|patient)",
    data=epilepsy,
    family="poisson"
)
print(priors)

Use with formula object:

from brmspy import brms

f = brms.formula("y ~ x + (1|group)")
priors = brms.default_prior(f, data=df, family="gaussian")