prior

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.fit() and brmspy.make_stancode().

Notes

This function does not validate the prior expression string itself — validation occurs inside brms. Its purpose is to construct a structured, editor-friendly representation that seamlessly maps to rpy2 calls.

Examples:

Prior on the intercept ::

p = prior("student_t(3, 0, 1.95)", class_="Intercept")

Prior on a coefficient ::

p = prior("normal(0, 1)", class_="b", coef="age")

Group-level (hierarchical) SD prior ::

p = prior("exponential(2)", class_="sd", group="region")

Truncated prior ::

p = prior("normal(0, 1)", class_="b", coef="income", lb=0)

Multiple priors passed to fit ::

from brmspy import prior
priors = [
    prior("student_t(3, 0, 2)", class_="b", coef="zAge"),
    prior("exponential(2)", class_="sd", group="patient"),
]
model = brms.fit("y ~ zAge + (1|patient)", data=df, priors=priors)

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

Get default priors for all model parameters.

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 FormulaResult	Model formula (e.g., "y ~ x + (1|group)") or FormulaResult object	required
data	DataFrame or dict	Dataset containing model variables. Required for data-dependent priors	None
family	str or ListVector	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 https://paulbuerkner.com/brms/reference/get_prior.html

Examples:

Review default priors for a model:

from brmspy import brms

priors = brms.get_prior(
    formula="count ~ zAge + zBase * Trt + (1|patient)",
    data=epilepsy,
    family="poisson"
)
print(priors)
#   prior       class    coef      group  ...
#   student_t() Intercept  ...    ...     ...
#   (flat)      b          zAge    ...    ...

Customize and use priors:

from brmspy import brms, prior

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

# Create custom priors based on review
custom_priors = [
    prior("normal(0, 0.5)", class_="b"),
    prior("exponential(2)", class_="sigma")
]

model = brms.fit("y ~ x", data=df, priors=custom_priors)

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

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

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 ListVector	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 ListVector	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 https://paulbuerkner.com/brms/reference/get_prior.html

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")