brm

Classes

Functions

brm(formula, data, priors=None, family='gaussian', sample_prior='no', sample=True, backend='cmdstanr', formula_args=None, cores=2, **brm_args)

Fit Bayesian regression model using brms.

Uses brms with cmdstanr backend for proper parameter naming. Returns FitResult with .idata (arviz.InferenceData) and .r (brmsfit) attributes.

BRMS documentation and parameters

Parameters:

Name	Type	Description	Default
formula	str	brms formula: formula string, e.g "y ~ x + (1|group)" or FormulaResult from formula()	required
data	dict or DataFrame	Model data	required
priors	list	Prior specifications: [("normal(0,1)", "b"), ("cauchy(0,2)", "sd")]	[]
family	str	Distribution family: "gaussian", "poisson", "binomial", etc.	"gaussian"
sample_prior	str	Sample from prior: "no", "yes", "only"	"no"
sample	bool	Whether to sample. If False, returns compiled model with empty=TRUE	True
backend	str	Stan backend: "cmdstanr" (recommended), "rstan"	"cmdstanr"
**brm_args		Additional brms::brm() arguments: chains=4, iter=2000, warmup=1000, cores=4, seed=123, thin=1, etc.	{}

Returns:

Type	Description
FitResult	Object with .idata (arviz.InferenceData) and .r (brmsfit) attributes

See Also

brms::brm : R documentation https://paulbuerkner.com/brms/reference/brm.html posterior_epred : Expected value predictions posterior_predict : Posterior predictive samples formula : Create formula object with options

Warnings

cores <= 1 is unsafe in embedded R sessions. The single-process code path used by brms::brm() can crash the interpreter. Always use cores >= 2 to force parallel workers and avoid segfaults.

Examples:

Basic Poisson regression:

from brmspy import brms
import arviz as az

epilepsy = brms.get_brms_data("epilepsy")
model = brms.fit(
    formula="count ~ zAge + zBase * Trt + (1|patient)",
    data=epilepsy,
    family="poisson",
    chains=4,
    iter=2000
)

az.summary(model.idata)

With custom priors:

from brmspy import prior

model = brms.fit(
    formula="count ~ zAge + zBase * Trt + (1|patient)",
    data=epilepsy,
    priors=[
        prior("normal(0, 0.5)", class_="b"),
        prior("exponential(2)", class_="sd", group="patient")
    ],
    family="poisson",
    chains=4
)

Survival model with censoring:

kidney = brms.get_brms_data("kidney")

survival_model = brms.fit(
    formula="time | cens(censored) ~ age + sex + disease + (1|patient)",
    data=kidney,
    family="weibull",
    chains=4,
    iter=4000,
    warmup=2000,
    cores=4,
    seed=42
)

Gaussian model with distributional regression:

    # Model both mean and variance
    model = brms.fit(
        formula=brms.formula(
            "y ~ x",
            sigma ~ "z"  # Model heteroscedasticity
        ),
        data=data,
        family="gaussian",
        chains=4
    )