Main Module

User-facing brms API.

Import this module to call brms functions from Python (for example brm, prior, posterior_predict, etc.). brmspy runs these calls through an isolated runtime so that R-side instability is less likely to take down your Python process.

Use brms.manage() to install brms / CmdStan, and to work with multiple isolated environments.

Examples:

from brmspy import brms
with brms.manage(environment_name="default") as ctx:
    ctx.install_brms(use_prebuilt=True)

Classes¶

`IDResult` `dataclass` ¶

Bases: Generic[T_idata], RListVectorExtension

Generic result container with arviz and R objects.

Attributes:

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

`LooResult` `dataclass` ¶

Bases: RListVectorExtension

Parsed brms::loo() result.

Attributes:

Name	Type	Description
`estimates, pointwise, diagnostics`	`DataFrame`	LOO tables.
`psis_object`	`Any or None`	PSIS object (if present). May be an R-handle wrapper depending on conversion.
`elpd_loo, p_loo, looic`	`float`	Key scalar metrics.
`se_elpd_loo, se_p_loo, se_looic`	`float`	Standard errors for the corresponding scalars.

Functions¶

`repr()` ¶

Pretty print LOO-CV results.

`LooCompareResult` `dataclass` ¶

Bases: RListVectorExtension

Result of comparing models by a LOO-style criterion.

Attributes:

Name	Type	Description
`table`	`DataFrame`	Comparison table.
`criterion`	`str`	Criterion name (e.g. `"loo"`).

`IDPosterior` ¶

Bases: IDConstantData

Typed .posterior extension to idata

`IDPosteriorPredictive` ¶

Bases: IDConstantData

Typed .posterior_predictive extension to idata

`IDPredictions` ¶

Bases: IDPredictionsConstantData

Typed .predictions extension to idata

`IDLogLikelihoodInsample` ¶

Bases: IDConstantData

Typed .log_likelihood extension to idata

`IDLogLikelihoodOutsample` ¶

Bases: IDPredictionsConstantData

Typed .log_likelihood extension to idata

`IDObservedData` ¶

Bases: IDConstantData

Typed .posterior extension to idata

`IDConstantData` ¶

Bases: InferenceData

Typed .constant_data extension to idata

`IDPredictionsConstantData` ¶

Bases: InferenceData

Typed .predictions_constant_data extension to idata

`RListVectorExtension` `dataclass` ¶

Generic result container with R objects.

Attributes:

Name	Type	Description
`r`	`ListVector`	R object from brms

`IDBrm` ¶

Bases: IDConstantData

Typed arviz.InferenceData for fitted brms models.

Extends arviz.InferenceData with type hints for IDE autocomplete. In brmspy, the fitted model result typically exposes an .idata attribute of this type.

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 (inherited from `arviz.InferenceData`).
`dims`	`dict`	Dimension specifications for variables (inherited from `arviz.InferenceData`).

See Also

brmspy.brms.brm : Creates fitted model results (alias: brmspy.brms.fit). arviz.InferenceData : Base class documentation.

Examples:

from brmspy import brms

model = brms.brm("y ~ x", data=df, chains=4)

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

`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

Examples:

Create prior specifications (prefer using prior()):

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)

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'}

`FormulaConstruct` `dataclass` ¶

A composite formula expression built from parts.

FormulaConstruct stores a tree of nodes (FormulaPart and/or R objects) representing expressions combined with +. It is primarily created by calling the public formula helpers exposed by brmspy.brms.

Notes

The + operator supports grouping:

a + b + c becomes a single summand (one “group”)
(a + b) + (a + b) becomes two summands (two “groups”)

Use iter_summands() to iterate over these groups in a deterministic way.

Functions¶

`add(other)` ¶

Combine two formula expressions with +.

Parameters:

Name	Type	Description	Default
`other`	`Other`	Value to add. Strings are treated as `bf(<string>)`.	required

Returns:

Type	Description
`FormulaConstruct`	New combined expression.

`radd(other)` ¶

Support "y ~ x" + bf("z ~ 1") by coercing the left operand.

`iter_summands()` ¶

Iterate over arithmetic groups (summands).

Returns:

Type	Description
`Iterator[tuple[FormulaPart \| ProxyListSexpVector, ...]]`	Each yielded tuple represents one summand/group.

Examples:

from brmspy.brms import bf, gaussian, set_rescor

f = bf("y ~ x") + gaussian() + set_rescor(True)
for summand in f.iter_summands():
    print(summand)

`iter()` ¶

Alias for iter_summands().

`iterate()` ¶

Iterate over all leaf nodes in left-to-right order.

This flattens the expression tree, unlike iter_summands(), which respects grouping.

Returns:

Type	Description
`Iterator[FormulaPart \| ProxyListSexpVector]`

`FormulaPart` `dataclass` ¶

A single formula helper invocation.

Instances of this type represent a call like bf("y ~ x") or set_rescor(True) without executing anything. They are primarily used as nodes inside a FormulaConstruct.

Parameters:

Name	Type	Description	Default
`_fun`	`Literal[...]`	Whitelisted formula helper name.	required
`_args`	`Sequence[Primitive]`	Positional arguments for the helper.	required
`_kwargs`	`Mapping[str, Primitive]`	Keyword arguments for the helper.	required

Notes

This is a low-level type. Most users should construct these via the public helper functions in brmspy.brms.

Functions¶

`__post_init__()` ¶

Validate _fun, _args, and _kwargs types after construction.

`str()` ¶

Render a readable fun(arg1, ..., kw=...) representation.

Functions¶

`log(*msg, method_name=None, level=logging.INFO)` ¶

Log a message with automatic method name detection.

Parameters:

Name	Type	Description	Default
`msg`	`str`	The message to log	`()`
`method_name`	`str`	The name of the method/function. If None, will auto-detect from call stack.	`None`
`level`	`int`	Logging level (default: logging.INFO)	`INFO`

`log_warning(msg, method_name=None)` ¶

Log a warning message.

Parameters:

Name	Type	Description	Default
`msg`	`str`	The warning message to log	required
`method_name`	`str`	The name of the method/function. If None, will auto-detect from call stack.	`None`

`get_brms_data(dataset_name, **kwargs)` ¶

Load an example dataset from the R brms package.

Parameters:

Name	Type	Description	Default
`dataset_name`	`str`	Dataset name (for example `"epilepsy"` or `"kidney"`).	required
`**kwargs`		Forwarded to R `utils::data()` via `get_data()`.	`{}`

Returns:

Type	Description
`DataFrame`	Dataset converted to a DataFrame.

Examples:

from brmspy import brms

epilepsy = brms.get_brms_data("epilepsy")
assert epilepsy.shape[0] > 0

`read_rds_fit(file, **kwargs)` ¶

Load a saved brms model from an .rds file.

Parameters:

Name	Type	Description	Default
`file`	`str`	Input path containing a saved brmsfit.	required
`**kwargs`		Forwarded to R `readRDS()`.	`{}`

Returns:

Type	Description
`FitResult`	`FitResult` containing ArviZ `InferenceData` and an underlying R handle.

Examples:

from brmspy import brms

fit = brms.read_rds_fit("model.rds")
fit.idata.posterior

`read_rds_raw(file, **kwargs)` ¶

Load an R object from an .rds file via R readRDS().

This returns the raw R object handle.

Parameters:

Name	Type	Description	Default
`file`	`str`	Input path.	required
`**kwargs`		Forwarded to R `readRDS()`.	`{}`

Returns:

Type	Description
`ListSexpVector`	Raw R object.

Examples:

from brmspy import brms

obj = brms.read_rds_raw("model.rds")

`save_rds(object, file, **kwargs)` ¶

Save an R object to an .rds file via R saveRDS().

Parameters:

Name	Type	Description	Default
`object`	`RListVectorExtension or ProxyListSexpVector`	Object to save. If you pass a `FitResult`, the underlying brmsfit is saved.	required
`file`	`str`	Output path.	required
`**kwargs`		Forwarded to R `saveRDS()` (for example `compress="xz"`).	`{}`

Returns:

Type	Description
`None`

Examples:

from brmspy import brms

model = brms.brm("y ~ x", data=df, chains=4)
brms.save_rds(model, "model.rds")

`get_data(dataset_name, **kwargs)` ¶

Load an R dataset and return it as a pandas DataFrame.

This is a thin wrapper around R's data() that loads the object into the R global environment and converts it to a :class:pandas.DataFrame.

Parameters:

Name	Type	Description	Default
`dataset_name`	`str`	Name of the dataset as used in R (e.g. `"BTdata"`).	required
`**kwargs`		Additional keyword arguments forwarded to R's `data()` function, for example `package="MCMCglmm"` or other arguments supported by `utils::data()` in R.	`{}`

Returns:

Type	Description
`DataFrame`	Dataset converted to a pandas DataFrame.

Raises:

Type	Description
`KeyError`	If the dataset is not found in the R global environment after calling `data()`.
`RuntimeError`	If conversion from the R object to a pandas DataFrame fails.

See Also

get_brms_data Convenience wrapper for datasets from the brms package.

`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")

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

brm(formula: FormulaConstruct | ProxyListSexpVector | str, data: dict | pd.DataFrame, priors: Sequence[PriorSpec] | None = ..., family: str | ListSexpVector | None = ..., sample_prior: str = ..., sample: bool = ..., backend: str = ..., formula_args: dict | None = ..., cores: int | None = ..., *, return_idata: Literal[True] = True, **brm_args: Any) -> FitResult

brm(formula: FormulaConstruct | ProxyListSexpVector | str, data: dict | pd.DataFrame, priors: Sequence[PriorSpec] | None = ..., family: str | ListSexpVector | None = ..., sample_prior: str = ..., sample: bool = ..., backend: str = ..., formula_args: dict | None = ..., cores: int | None = ..., *, return_idata: Literal[False], **brm_args: Any) -> ProxyListSexpVector

Fit a Bayesian regression model with brms.

This is a thin wrapper around R brms::brm() that returns a structured FitResult (including an ArviZ InferenceData).

Parameters:

Name	Type	Description	Default
`formula`	`str or FormulaConstruct`	Model formula. Accepts a plain brms formula string (e.g. `"y ~ x + (1\|g)"`) or a composed formula created via `brmspy.brms.bf()` / `brmspy.brms.lf()` (typically imported as `from brmspy.brms import bf, lf`).	required
`data`	`dict or DataFrame`	Model data.	required
`priors`	`Sequence[PriorSpec] or None`	Optional prior specifications created via `brmspy.brms.prior()`.	`None`
`family`	`str or ListSexpVector or None`	brms family specification (e.g. `"gaussian"`, `"poisson"`).	`"gaussian"`
`sample_prior`	`str`	Passed to brms. Common values: `"no"`, `"yes"`, `"only"`.	`"no"`
`sample`	`bool`	If `False`, compile the model without sampling (brms `empty=TRUE`).	`True`
`backend`	`str`	Stan backend. Common values: `"cmdstanr"` or `"rstan"`.	`"cmdstanr"`
`formula_args`	`dict or None`	Reserved for future use. Currently ignored.	`None`
`cores`	`int or None`	Number of cores for brms/cmdstanr.	`2`
`return_idata`	`bool`	When working with large datasets, you might not want the full idata. when False, you get the R object proxy which can be forwarded to posterior_epred or other functions	`True`
`**brm_args`		Additional keyword arguments passed to R `brms::brm()` (e.g. `chains`, `iter`, `warmup`, `seed`).	`{}`

Returns:

Type	Description
`FitResult`	Result object with `idata` (ArviZ `InferenceData`) and an underlying R handle.

See Also

brms::brm : R documentation

Warnings

Using cores <= 1 can be unstable in embedded R sessions and may crash the worker process. Prefer cores >= 2.

Examples:

from brmspy import brms

fit = brms.brm("y ~ x + (1|g)", data=df, family="gaussian", chains=4, cores=4)

fit.idata.posterior

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

brm(formula: FormulaConstruct | ProxyListSexpVector | str, data: dict | pd.DataFrame, priors: Sequence[PriorSpec] | None = ..., family: str | ListSexpVector | None = ..., sample_prior: str = ..., sample: bool = ..., backend: str = ..., formula_args: dict | None = ..., cores: int | None = ..., *, return_idata: Literal[True] = True, **brm_args: Any) -> FitResult

brm(formula: FormulaConstruct | ProxyListSexpVector | str, data: dict | pd.DataFrame, priors: Sequence[PriorSpec] | None = ..., family: str | ListSexpVector | None = ..., sample_prior: str = ..., sample: bool = ..., backend: str = ..., formula_args: dict | None = ..., cores: int | None = ..., *, return_idata: Literal[False], **brm_args: Any) -> ProxyListSexpVector

Fit a Bayesian regression model with brms.

This is a thin wrapper around R brms::brm() that returns a structured FitResult (including an ArviZ InferenceData).

Parameters:

Name	Type	Description	Default
`formula`	`str or FormulaConstruct`	Model formula. Accepts a plain brms formula string (e.g. `"y ~ x + (1\|g)"`) or a composed formula created via `brmspy.brms.bf()` / `brmspy.brms.lf()` (typically imported as `from brmspy.brms import bf, lf`).	required
`data`	`dict or DataFrame`	Model data.	required
`priors`	`Sequence[PriorSpec] or None`	Optional prior specifications created via `brmspy.brms.prior()`.	`None`
`family`	`str or ListSexpVector or None`	brms family specification (e.g. `"gaussian"`, `"poisson"`).	`"gaussian"`
`sample_prior`	`str`	Passed to brms. Common values: `"no"`, `"yes"`, `"only"`.	`"no"`
`sample`	`bool`	If `False`, compile the model without sampling (brms `empty=TRUE`).	`True`
`backend`	`str`	Stan backend. Common values: `"cmdstanr"` or `"rstan"`.	`"cmdstanr"`
`formula_args`	`dict or None`	Reserved for future use. Currently ignored.	`None`
`cores`	`int or None`	Number of cores for brms/cmdstanr.	`2`
`return_idata`	`bool`	When working with large datasets, you might not want the full idata. when False, you get the R object proxy which can be forwarded to posterior_epred or other functions	`True`
`**brm_args`		Additional keyword arguments passed to R `brms::brm()` (e.g. `chains`, `iter`, `warmup`, `seed`).	`{}`

Returns:

Type	Description
`FitResult`	Result object with `idata` (ArviZ `InferenceData`) and an underlying R handle.

See Also

brms::brm : R documentation

Warnings

Using cores <= 1 can be unstable in embedded R sessions and may crash the worker process. Prefer cores >= 2.

Examples:

from brmspy import brms

fit = brms.brm("y ~ x + (1|g)", data=df, family="gaussian", chains=4, cores=4)

fit.idata.posterior

`summary(model, **kwargs)` ¶

Generate comprehensive summary statistics for a fitted brms model.

Returns a SummaryResult dataclass containing model information, parameter estimates, and diagnostic information. The SummaryResult object provides pretty printing via str() or print() and structured access to all components.

BRMS documentation and parameters

Parameters:

Name	Type	Description	Default
`model`	`FitResult`	Fitted model returned by `brmspy.brms.brm()`.	required
`**kwargs`		Additional arguments passed to brms::summary(), such as: - probs: Quantiles for credible intervals, e.g., `probs=(0.025, 0.975)` - robust: Use robust estimates (median, MAD), default False	`{}`

Returns:

Type Description

SummaryResult

A dataclass containing:

formula (str): Model formula as string
data_name (str): Name of the data object used
group (str): Grouping structure information
nobs (int): Number of observations
ngrps (Dict[str, int]): Number of groups per grouping variable
autocor (Optional[dict]): Autocorrelation structure if present
prior (pd.DataFrame): Prior specifications used
algorithm (str): Sampling algorithm (e.g., "sampling")
sampler (str): Sampler specification (e.g., "sample(hmc)")
total_ndraws (int): Total number of post-warmup draws
chains (float): Number of chains
iter (float): Iterations per chain
warmup (float): Warmup iterations per chain
thin (float): Thinning interval
has_rhat (bool): Whether Rhat diagnostics are reported
fixed (pd.DataFrame): Population-level (fixed) effects estimates
spec_pars (pd.DataFrame): Family-specific parameters (e.g., sigma)
cor_pars (pd.DataFrame): Correlation parameters if present
random (dict): Group-level (random) effects by grouping variable

See Also

brms::summary.brmsfit : R documentation

Examples:

Basic usage with pretty printing:

import brmspy

model = brmspy.fit("y ~ x", data=data, chains=4)
summary = brmspy.summary(model)

# Pretty print full summary
print(summary)

Access specific components:

# Get population-level effects as DataFrame
fixed_effects = summary.fixed
print(fixed_effects)

# Get family-specific parameters (e.g., sigma)
spec_params = summary.spec_pars
print(spec_params)

# Access random effects (if present)
random_effects = summary.random
for group_name, group_df in random_effects.items():
    print(f"Random effects for {group_name}:")
    print(group_df)

# Check model metadata
print(f"Formula: {summary.formula}")
print(f"Total draws: {summary.total_ndraws}")
print(f"Rhat reported: {summary.has_rhat}")

Custom credible intervals:

# Use 90% credible intervals instead of default 95%
summary_90 = brmspy.summary(model, probs=(0.05, 0.95))
print(summary_90.fixed)

`fixef(object, summary=True, robust=False, probs=(0.025, 0.975), pars=None, **kwargs)` ¶

Extract population-level (fixed) effects estimates from a fitted brms model.

Returns a pandas DataFrame containing estimates and uncertainty intervals for all population-level parameters (fixed effects). By default, returns summary statistics (mean, standard error, credible intervals). Can also return raw posterior samples when summary=False.

BRMS documentation

Parameters:

Name	Type	Description	Default
`object`	`FitResult or ListSexpVector`	Fitted model returned by `brmspy.brms.brm()` or an R brmsfit object.	required
`summary`	`bool`	If True, return summary statistics (mean/median, SE/MAD, credible intervals). If False, return matrix of posterior samples (iterations × parameters).	`True`
`robust`	`bool`	If True, use median and MAD instead of mean and SD for summary statistics. Only used when `summary=True`.	`False`
`probs`	`tuple of float`	Quantiles for credible intervals, e.g., (0.025, 0.975) for 95% intervals. Only used when `summary=True`.	`(0.025, 0.975)`
`pars`	`list of str`	Specific parameter names to extract. If None, returns all fixed effects. Useful for subsetting when you only need specific coefficients.	`None`
`**kwargs`		Additional arguments passed to brms::fixef()	`{}`

Returns:

Type	Description
`DataFrame`	When `summary=True` (default): DataFrame with parameters as rows and columns for Estimate, Est.Error, Q2.5, Q97.5 (or other quantiles specified in `probs`), and optionally Rhat and Bulk_ESS/Tail_ESS diagnostics. When `summary=False`: DataFrame with posterior samples where rows are iterations and columns are parameters. Shape is (n_iterations × n_parameters).

See Also

brms::fixef.brmsfit : R documentation summary() : Full model summary with all parameter types

Examples:

Basic usage with summary statistics:

import brmspy

model = brmspy.fit("y ~ x1 + x2", data=data, chains=4)

# Get fixed effects summary
fixed_effects = brmspy.fixef(model)
print(fixed_effects)
#             Estimate  Est.Error      Q2.5     Q97.5
# Intercept  10.234567   0.123456  9.992345  10.47689
# x1          0.456789   0.098765  0.263456   0.65012
# x2         -0.234567   0.087654 -0.406789  -0.06234

Get specific parameters only:

# Extract only specific coefficients
x1_x2_effects = brmspy.fixef(model, pars=["x1", "x2"])
print(x1_x2_effects)

Use robust estimates (median and MAD):

# Use median and MAD instead of mean and SD
robust_effects = brmspy.fixef(model, robust=True)
print(robust_effects)

Custom credible intervals:

# Get 90% credible intervals
effects_90 = brmspy.fixef(model, probs=(0.05, 0.95))
print(effects_90)

Get raw posterior samples:

# Get full posterior samples matrix
samples = brmspy.fixef(model, summary=False)
print(samples.shape)  # (n_iterations, n_parameters)

# Can then compute custom statistics
import numpy as np
custom_quantile = np.percentile(samples["x1"], 90)

`ranef(object, summary=True, robust=False, probs=(0.025, 0.975), pars=None, groups=None, **kwargs)` ¶

Extract group-level (random) effects as xarray DataArrays.

This is a wrapper around brms::ranef(). For summary=True (default), each grouping factor is returned as a 3D array with dimensions ("group", "stat", "coef"). For summary=False, each factor is returned as ("draw", "group", "coef") with one slice per posterior draw.

Parameters:

Name	Type	Description	Default
`object`	`FitResult or ListVector`	Fitted model returned by :func:`brmspy.brms.fit` or an R `brmsfit` object / summary list.	required
`summary`	`bool`	If True, return posterior summaries for the group-level effects (means, errors, intervals). If False, return per-draw random effects.	`True`
`robust`	`bool`	If True, use robust summaries (median and MAD) instead of mean and SD. Passed through to `brms::ranef()` when `summary=True`.	`False`
`probs`	`tuple of float`	Central posterior interval probabilities, as in `brms::ranef()`. Only used when `summary=True`.	`(0.025, 0.975)`
`pars`	`str or sequence of str`	Subset of group-level parameters to include. Passed to `brms::ranef()`.	`None`
`groups`	`str or sequence of str`	Subset of grouping factors to include. Passed to `brms::ranef()`.	`None`
`**kwargs`		Additional keyword arguments forwarded to `brms::ranef()`.	`{}`

Returns:

Type	Description
`dict[str, DataArray]`	Mapping from grouping-factor name (e.g. `"patient"`) to a `DataArray`: `summary=True`: dims `("group", "stat", "coef")`, with `stat` typically containing `["Estimate", "Est.Error", "Q2.5", "Q97.5"]`. `summary=False`: dims `("draw", "group", "coef")`, where `draw` indexes posterior samples.

Examples:

Compute summary random effects and inspect all coefficients for a single group level:

from brmspy import brms
from brmspy.brms import ranef

fit = brms.fit("count ~ zAge + zBase * Trt + (1 + zBase + Trt | patient)",
               data=data, family="poisson")

re = ranef(fit)  # summary=True by default
patient_re = re["patient"].sel(group="1", stat="Estimate")

Extract per-draw random effects for downstream MCMC analysis:

re_draws = ranef(fit, summary=False)
patient_draws = re_draws["patient"]       # dims: ("draw", "group", "coef")
first_draw = patient_draws.sel(draw=0)

`posterior_summary(object, variable=None, probs=(0.025, 0.975), robust=False, **kwargs)` ¶

Extract posterior summary statistics for all or selected model parameters.

Provides a DataFrame with estimates, standard errors, and credible intervals for all parameters in a brms model, including fixed effects, random effects, and auxiliary parameters. More comprehensive than fixef() or ranef() as it covers all parameter types.

BRMS documentation

Parameters:

Name	Type	Description	Default
`object`	`FitResult or ListSexpVector`	Fitted model returned by `brmspy.brms.brm()` or an R brmsfit object.	required
`variable`	`str or list of str`	Specific variable name(s) to extract. If None, returns all parameters. Supports regex patterns for flexible selection.	`None`
`probs`	`tuple of float`	Quantiles for credible intervals, e.g., (0.025, 0.975) for 95% intervals.	`(0.025, 0.975)`
`robust`	`bool`	If True, use median and MAD instead of mean and SD for summary statistics.	`False`
`**kwargs`		Additional arguments passed to brms::posterior_summary()	`{}`

Returns:

Type	Description
`DataFrame`	DataFrame with parameters as rows and columns for Estimate, Est.Error, and quantiles (e.g., Q2.5, Q97.5). Includes all model parameters: population-level effects, group-level effects, and auxiliary parameters.

See Also

brms::posterior_summary : R documentation https://paulbuerkner.com/brms/reference/posterior_summary.brmsfit.html fixef() : Extract only population-level effects ranef() : Extract only group-level effects

Examples:

Get summary for all parameters:

import brmspy

model = brmspy.fit("y ~ x1 + (1|group)", data=data, chains=4)

# Get all parameter summaries
all_params = brmspy.posterior_summary(model)
print(all_params)

Extract specific parameters:

# Get summary for specific parameters
intercept = brmspy.posterior_summary(model, variable="b_Intercept")
print(intercept)

# Multiple parameters
fixed_only = brmspy.posterior_summary(model, variable=["b_Intercept", "b_x1"])
print(fixed_only)

Custom credible intervals with robust estimates:

# 90% intervals with median/MAD
robust_summary = brmspy.posterior_summary(
    model,
    probs=(0.05, 0.95),
    robust=True
)
print(robust_summary)

`prior_summary(object, all=True, **kwargs)` ¶

Extract prior specifications used in a fitted brms model.

Returns a DataFrame containing all prior distributions that were used (either explicitly set or defaults) when fitting the model. Useful for documenting model specifications and understanding which priors were applied.

BRMS documentation

Parameters:

Name	Type	Description	Default
`object`	`FitResult or ListVector`	Fitted model returned by `brmspy.brms.brm()` or an R brmsfit object.	required
`all`	`bool`	If True, return all priors including default priors. If False, return only explicitly set priors.	`True`
`**kwargs`		Additional arguments passed to brms::prior_summary()	`{}`

Returns:

Type Description

DataFrame

DataFrame with columns describing prior specifications: - prior: Prior distribution formula - class: Parameter class (b, sd, Intercept, etc.) - coef: Specific coefficient (if applicable) - group: Grouping factor (if applicable) - resp: Response variable (for multivariate models) - dpar: Distributional parameter (if applicable) - nlpar: Non-linear parameter (if applicable) - lb/ub: Bounds for truncated priors - source: Origin of prior (default, user, etc.)

See Also

brms::prior_summary : R documentation https://paulbuerkner.com/brms/reference/prior_summary.brmsfit.html get_prior() : Get prior structure before fitting default_prior() : Get default priors for a model

Examples:

Get all priors used in a model:

import brmspy

model = brmspy.fit(
    "y ~ x1 + (1|group)",
    data=data,
    priors=[brmspy.prior("normal(0, 1)", "b")],
    chains=4
)

# Get all priors (including defaults)
priors = brmspy.prior_summary(model)
print(priors)

Get only explicitly set priors:

# Get only user-specified priors
user_priors = brmspy.prior_summary(model, all=False)
print(user_priors)

Compare with what would be used before fitting:

# Before fitting - check default priors
default_priors = brmspy.get_prior("y ~ x1", data=data)

# After fitting - see what was actually used
used_priors = brmspy.prior_summary(model)

`validate_newdata(newdata, object, re_formula=None, allow_new_levels=False, newdata2=None, resp=None, check_response=True, incl_autocor=True, group_vars=None, req_vars=None, **kwargs)` ¶

Validate new data for predictions from a fitted brms model.

Ensures that new data contains all required variables and has the correct structure for making predictions. Checks variable types, factor levels, grouping variables, and autocorrelation structures. This function is primarily used internally by prediction methods but can be called directly for debugging or validation purposes.

BRMS documentation

Parameters:

Name	Type	Description	Default
`newdata`	`DataFrame`	DataFrame containing new data to be validated against the model. Must include all predictor variables used in the model formula.	required
`object`	`FitResult or ListSexpVector`	Fitted model returned by `brmspy.brms.brm()` or an R brmsfit object.	required
`re_formula`	`str`	Formula string specifying group-level effects to include in validation. If None (default), include all group-level effects. If NA, include no group-level effects.	`None`
`allow_new_levels`	`bool`	Whether to allow new levels of grouping variables not present in the original training data. If False, raises an error for new levels.	`False`
`newdata2`	`DataFrame`	Additional data that cannot be passed via `newdata`, such as objects used in autocorrelation structures or stanvars.	`None`
`resp`	`str or list of str`	Names of response variables to validate. If specified, validation is performed only for the specified responses (relevant for multivariate models).	`None`
`check_response`	`bool`	Whether to check if response variables are present in newdata. Set to False when making predictions where response is not needed.	`True`
`incl_autocor`	`bool`	Whether to include autocorrelation structures originally specified in the model. If True, validates autocorrelation-related variables.	`True`
`group_vars`	`list of str`	Names of specific grouping variables to validate. If None (default), validates all grouping variables present in the model.	`None`
`req_vars`	`list of str`	Names of specific variables required in newdata. If None (default), all variables from the original training data are required (unless excluded by other parameters).	`None`
`**kwargs`		Additional arguments passed to brms::validate_newdata()	`{}`

Returns:

Type	Description
`DataFrame`	Validated DataFrame based on newdata, potentially with added or modified columns to ensure compatibility with the model.

Raises:

Type	Description
`ValueError`	If newdata is missing required variables
`ValueError`	If factor levels in newdata don't match those in training data (when allow_new_levels=False)
`ValueError`	If grouping variables have invalid structure

See Also

brms::validate_newdata : R documentation https://paulbuerkner.com/brms/reference/validate_newdata.html posterior_predict() : Uses validate_newdata internally posterior_epred() : Uses validate_newdata internally

Examples:

Basic validation for prediction data:

import brmspy
import pandas as pd

# Fit model
model = brmspy.fit("y ~ x1 + x2", data=train_data, chains=4)

# Prepare new data
new_data = pd.DataFrame({
    'x1': [1.0, 2.0, 3.0],
    'x2': [0.5, 1.0, 1.5]
})

# Validate before prediction
validated_data = brmspy.validate_newdata(new_data, model)
print(validated_data)

Validate with group-level effects:

# Model with random effects
model = brmspy.fit("y ~ x + (1|group)", data=train_data, chains=4)

# New data with grouping variable
new_data = pd.DataFrame({
    'x': [1.0, 2.0],
    'group': ['A', 'B']  # Must match training data groups
})

# Validate - will error if groups A or B weren't in training
validated_data = brmspy.validate_newdata(
    new_data,
    model,
    allow_new_levels=False
)

Allow new levels for population-level predictions:

# Allow new group levels (makes population-level predictions only)
new_data_with_new_groups = pd.DataFrame({
    'x': [3.0, 4.0],
    'group': ['C', 'D']  # New groups not in training
})

validated_data = brmspy.validate_newdata(
    new_data_with_new_groups,
    model,
    allow_new_levels=True
)

Skip response variable checking:

# When making predictions, response not needed
new_data = pd.DataFrame({'x1': [1.0, 2.0]})

validated_data = brmspy.validate_newdata(
    new_data,
    model,
    check_response=False
)

`call(function, *args, **kwargs)` ¶

Call an R function by name with brmspy type conversion.

This is intended as an escape hatch for R/brms functionality that does not yet have a dedicated wrapper.

Parameters:

Name	Type	Description	Default
`function`	`str`	Function name. If not namespaced, brmspy tries `brms::<function>` first, then falls back to evaluating the name directly (e.g. `"stats::AIC"`).	required
`*args`		Positional arguments.	`()`
`**kwargs`		Keyword arguments.	`{}`

Returns:

Type	Description
`Any`	Converted return value.

Examples:

>>> from brmspy import brms
>>> fit = brms.brm("y ~ x", data=df, chains=4)
>>> aic = brms.call("stats::AIC", fit)

`bf(*formulas, **formula_args)` ¶

Build a brms model formula.

This is the primary entrypoint for specifying the mean model and can be combined with other formula parts (e.g. lf, nlf, acformula) using +.

Parameters:

Name	Type	Description	Default
`*formulas`	`str`	One or more brms formula strings (e.g. `"y ~ x + (1\|group)"`). Multiple formulas are commonly used for multivariate models.	`()`
`**formula_args`		Keyword arguments forwarded to R `brms::brmsformula()` (for example `decomp="QR"`, `center=True`, `sparse=True`, `nl=True`, `loop=True`).	`{}`

Returns:

Type	Description
`FormulaConstruct`	A composable formula specification.

See Also

brms::brmsformula : R documentation

Examples:

Basic formula:

from brmspy.brms import bf

f = bf("y ~ x1 + x2 + (1|group)")

QR decomposition (often helps with collinearity):

from brmspy.brms import bf

f = bf("reaction ~ days + (days|subject)", decomp="QR")

Multivariate formula + residual correlation:

from brmspy.brms import bf, set_rescor

f = bf("mvbind(y1, y2) ~ x") + set_rescor(True)

`lf(*formulas, flist=None, dpar=None, resp=None, center=None, cmc=None, sparse=None, decomp=None)` ¶

Add linear formulas for distributional / non-linear parameters.

This wraps R brms::lf() and is typically used to model distributional parameters such as sigma (heteroskedasticity) or to specify predictors for non-linear parameters.

Parameters:

Name	Type	Description	Default
`*formulas`	`str \| FormulaConstruct \| FormulaPart \| ProxyListSexpVector`	One or more formulas such as `"sigma ~ x"`.	`()`
`flist`		Optional list of formulas (advanced; mirrors brms).	`None`
`dpar`	`str or None`	Distributional parameter name (e.g. `"sigma"`, `"phi"`).	`None`
`resp`	`str or None`	Response name for multivariate models.	`None`
`center`	`bool \| None`	Forwarded to R `brms::lf()`.	`None`
`cmc`	`bool \| None`	Forwarded to R `brms::lf()`.	`None`
`sparse`	`bool \| None`	Forwarded to R `brms::lf()`.	`None`
`decomp`	`bool \| None`	Forwarded to R `brms::lf()`.	`None`

Returns:

Type	Description
`FormulaConstruct`	A composable formula specification that can be combined using `+`.

See Also

brms::lf : R documentation

Examples:

Model mean + sigma:

from brmspy.brms import bf, lf

f = bf("y ~ x") + lf("sigma ~ x", dpar="sigma")

`nlf(*formulas, flist=None, dpar=None, resp=None, loop=None)` ¶

Add non-linear formulas.

Wraps R brms::nlf(). This is used together with set_nl() and parameter definitions in lf() to specify non-linear models.

Parameters:

Name	Type	Description	Default
`*formulas`	`str \| FormulaConstruct \| FormulaPart \| ProxyListSexpVector`	One or more non-linear formulas (e.g. `"y ~ a * exp(b * x)"`).	`()`
`flist`		Optional list of formulas (advanced; mirrors brms).	`None`
`dpar`	`str or None`	Distributional parameter name (optional).	`None`
`resp`	`str or None`	Response name for multivariate models.	`None`
`loop`	`bool or None`	Forwarded to R `brms::nlf(loop=...)`.	`None`

Returns:

Type	Description
`FormulaConstruct`	A composable formula specification.

See Also

brms::nlf : R documentation

Examples:

from brmspy.brms import bf, nlf, set_nl

f = bf("y ~ 1") + nlf("y ~ a * exp(b * x)") + set_nl()

`acformula(autocor, resp=None)` ¶

Add an autocorrelation structure.

Wraps R brms::acformula().

Parameters:

Name	Type	Description	Default
`autocor`	`str`	One-sided autocorrelation formula (e.g. `"~ arma(p = 1, q = 1)"`).	required
`resp`	`str or None`	Response name for multivariate models.	`None`

Returns:

Type	Description
`FormulaConstruct`	A composable formula specification.

See Also

brms::acformula : R documentation

Examples:

from brmspy.brms import bf, acformula

f = bf("y ~ x") + acformula("~ arma(p = 1, q = 1)")

`set_rescor(rescor=True)` ¶

Control residual correlations in multivariate models.

Wraps R brms::set_rescor().

Parameters:

Name	Type	Description	Default
`rescor`	`bool`	Whether to model residual correlations.	`True`

Returns:

Type	Description
`FormulaConstruct`	A composable formula specification.

See Also

brms::set_rescor : R documentation

Examples:

from brmspy.brms import bf, set_rescor

f = bf("y1 ~ x") + bf("y2 ~ z") + set_rescor(True)

`set_mecor(mecor=True)` ¶

Control correlations between latent me() terms.

Wraps R brms::set_mecor().

Parameters:

Name	Type	Description	Default
`mecor`	`bool`	Whether to model correlations between latent variables introduced by `me()`.	`True`

Returns:

Type	Description
`FormulaConstruct`	A composable formula specification.

See Also

brms::set_mecor : R documentation

Examples:

from brmspy.brms import bf, set_mecor

f = bf("y ~ me(x, sdx)") + set_mecor(True)

`set_nl(dpar=None, resp=None)` ¶

Mark a model (or part of it) as non-linear.

Wraps R brms::set_nl().

Parameters:

Name	Type	Description	Default
`dpar`	`str or None`	Distributional parameter name (if only part of the model is non-linear).	`None`
`resp`	`str or None`	Response name for multivariate models.	`None`

Returns:

Type	Description
`FormulaConstruct`	A composable formula specification.

See Also

brms::set_nl : R documentation

Examples:

from brmspy.brms import bf, lf, set_nl

f = bf("y ~ a * inv_logit(x * b)") + lf("a + b ~ z") + set_nl()

`formula(*formulas, **formula_args)` ¶

Build a brms model formula.

This is the primary entrypoint for specifying the mean model and can be combined with other formula parts (e.g. lf, nlf, acformula) using +.

Parameters:

Name	Type	Description	Default
`*formulas`	`str`	One or more brms formula strings (e.g. `"y ~ x + (1\|group)"`). Multiple formulas are commonly used for multivariate models.	`()`
`**formula_args`		Keyword arguments forwarded to R `brms::brmsformula()` (for example `decomp="QR"`, `center=True`, `sparse=True`, `nl=True`, `loop=True`).	`{}`

Returns:

Type	Description
`FormulaConstruct`	A composable formula specification.

See Also

brms::brmsformula : R documentation

Examples:

Basic formula:

from brmspy.brms import bf

f = bf("y ~ x1 + x2 + (1|group)")

QR decomposition (often helps with collinearity):

from brmspy.brms import bf

f = bf("reaction ~ days + (days|subject)", decomp="QR")

Multivariate formula + residual correlation:

from brmspy.brms import bf, set_rescor

f = bf("mvbind(y1, y2) ~ x") + set_rescor(True)

`posterior_epred(model, newdata=None, **kwargs)` ¶

posterior_epred(model: FitResult | ProxyListSexpVector, newdata: Literal[None] = None) -> IDResult[IDPosterior]

posterior_epred(model: FitResult | ProxyListSexpVector, newdata: pd.DataFrame) -> IDResult[IDPredictions]

Compute expected posterior predictions (noise-free).

Wrapper around R brms::posterior_epred(). This returns draws of the expected value (typically on the response scale), without observation noise.

Parameters:

Name	Type	Description	Default
`model`	`FitResult`	Fitted model.	required
`newdata`	`DataFrame or None`	New data for predictions. If `None`, uses the training data.	`None`
`**kwargs`		Forwarded to `brms::posterior_epred()`.	`{}`

Returns:

Type	Description
`PosteriorEpredResult`	Result containing `idata` (ArviZ `InferenceData`) and an underlying R handle.

See Also

brms::posterior_epred : R documentation

Examples:

from brmspy import brms

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

ep.idata.posterior

`posterior_linpred(model, newdata=None, **kwargs)` ¶

posterior_linpred(model: FitResult | ProxyListSexpVector, newdata: Literal[None] = None, **kwargs) -> IDResult[IDPosterior]

posterior_linpred(model: FitResult | ProxyListSexpVector, newdata: pd.DataFrame, **kwargs) -> IDResult[IDPredictions]

Draw from the linear predictor.

Wrapper around R brms::posterior_linpred(). This typically returns draws on the link scale (before applying the inverse link), unless you pass transform=True.

Parameters:

Name	Type	Description	Default
`model`	`FitResult`	Fitted model.	required
`newdata`	`DataFrame or None`	New data for predictions. If `None`, uses the training data.	`None`
`**kwargs`		Forwarded to `brms::posterior_linpred()` (commonly `transform` or `ndraws`).	`{}`

Returns:

Type	Description
`PosteriorLinpredResult`	Result containing `idata` (ArviZ `InferenceData`) and an underlying R handle.

See Also

brms::posterior_linpred : R documentation

Examples:

from brmspy import brms

fit = brms.brm("y ~ x", data=df, chains=4)
lp = brms.posterior_linpred(fit, transform=False)

lp.idata.predictions

`posterior_predict(model, newdata=None, **kwargs)` ¶

posterior_predict(model: FitResult | ProxyListSexpVector, newdata: Literal[None] = None, **kwargs) -> IDResult[IDPosteriorPredictive]

posterior_predict(model: FitResult | ProxyListSexpVector, newdata: pd.DataFrame, **kwargs) -> IDResult[IDPredictions]

Draw from the posterior predictive distribution (includes observation noise).

Wrapper around R brms::posterior_predict().

Parameters:

Name	Type	Description	Default
`model`	`FitResult`	Fitted model.	required
`newdata`	`DataFrame or None`	New data for predictions. If `None`, uses the training data.	`None`
`**kwargs`		Forwarded to `brms::posterior_predict()`.	`{}`

Returns:

Type	Description
`PosteriorPredictResult`	Result containing `idata` (ArviZ `InferenceData`) and an underlying R handle.

See Also

brms::posterior_predict : R documentation

Examples:

from brmspy import brms

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

pp.idata.posterior_predictive

`log_lik(model, newdata=None, **kwargs)` ¶

log_lik(model: FitResult | ProxyListSexpVector, newdata: Literal[None] = None, **kwargs) -> IDResult[IDLogLikelihoodInsample]

log_lik(model: FitResult | ProxyListSexpVector, newdata: pd.DataFrame, **kwargs) -> IDResult[IDLogLikelihoodOutsample]

Compute pointwise log-likelihood draws.

Wrapper around R brms::log_lik(). The result is useful for LOO/WAIC via ArviZ.

Parameters:

Name	Type	Description	Default
`model`	`FitResult`	Fitted model.	required
`newdata`	`DataFrame or None`	New data. If `None`, uses the training data.	`None`
`**kwargs`		Forwarded to `brms::log_lik()`.	`{}`

Returns:

Type	Description
`LogLikResult`	Result containing `idata` (ArviZ `InferenceData`) and an underlying R handle.

See Also

brms::log_lik : R documentation

Examples:

from brmspy import brms
import arviz as az

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

az.loo(ll.idata)

`posterior(model, **kwargs)` ¶

Return posterior draws as idata.

Wrapper around R posterior::as_draws_df().

Parameters:

Name	Type	Description	Default
`model`	`FitResult`	Fitted model.	required
`**kwargs`		Forwarded to `posterior::as_draws_df()`. e.g inc_warmup, regex, variable	`{}`

Returns:

Type	Description
`PosteriorEpredResult`	Result containing `idata` (ArviZ `InferenceData`) and an underlying R handle.

Examples:

from brmspy import brms

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

ep.idata.posterior

`make_stancode(formula, data, priors=None, family='poisson', sample_prior='no', formula_args=None)` ¶

Generate Stan code using R brms::make_stancode().

Useful for inspecting the generated Stan model before fitting.

Parameters:

Name	Type	Description	Default
`formula`	`str or FormulaConstruct`	Model formula.	required
`data`	`DataFrame`	Model data.	required
`priors`	`Sequence[PriorSpec] or None`	Optional prior specifications created via `brmspy.brms.prior()`.	`None`
`family`	`str`	Distribution family (e.g. `"gaussian"`, `"poisson"`).	`"poisson"`
`sample_prior`	`str`	Prior sampling mode passed to brms (`"no"`, `"yes"`, `"only"`).	`"no"`
`formula_args`	`dict or None`	Reserved for future use. Currently ignored.	`None`

Returns:

Type	Description
`str`	Complete Stan program as a string.

See Also

brms::make_stancode : R documentation

Examples:

from brmspy import brms

epilepsy = brms.get_brms_data("epilepsy")
code = brms.make_stancode(
    "count ~ zAge + zBase * Trt + (1|patient)",
    data=epilepsy,
    family="poisson",
)

assert isinstance(code, str)

`brmsfamily(family, link=None, link_sigma='log', link_shape='log', link_nu='logm1', link_phi='log', link_kappa='log', link_beta='log', link_zi='logit', link_hu='logit', link_zoi='logit', link_coi='logit', link_disc='log', link_bs='log', link_ndt='log', link_bias='logit', link_xi='log1p', link_alpha='identity', link_quantile='logit', threshold='flexible', refcat=None, **kwargs)` ¶

Family objects provide a convenient way to specify the details of the models used by many model fitting functions. The family functions presented here are for use with brms only and will not work with other model fitting functions such as glm or glmer. However, the standard family functions as described in family will work with brms.

Parameters:

Name	Type	Description	Default
`family`		A character string naming the distribution family of the response variable to be used in the model. Currently, the following families are supported: gaussian, student, binomial, bernoulli, beta-binomial, poisson, negbinomial, geometric, Gamma, skew_normal, lognormal, shifted_lognormal, exgaussian, wiener, inverse.gaussian, exponential, weibull, frechet, Beta, dirichlet, von_mises, asym_laplace, gen_extreme_value, categorical, multinomial, cumulative, cratio, sratio, acat, hurdle_poisson, hurdle_negbinomial, hurdle_gamma, hurdle_lognormal, hurdle_cumulative, zero_inflated_binomial, zero_inflated_beta_binomial, zero_inflated_beta, zero_inflated_negbinomial, zero_inflated_poisson, and zero_one_inflated_beta.	required
`link`	`str \| None`	A specification for the model link function. This can be a name/expression or character string. See the 'Details' section for more information on link functions supported by each family.	`None`
`link_sigma`	`str`	Link of auxiliary parameter sigma if being predicted.	`'log'`
`link_shape`	`str`	Link of auxiliary parameter shape if being predicted.	`'log'`
`link_nu`	`str`	Link of auxiliary parameter nu if being predicted.	`'logm1'`
`link_phi`	`str`	Link of auxiliary parameter phi if being predicted.	`'log'`
`link_kappa`	`str`	Link of auxiliary parameter kappa if being predicted.	`'log'`
`link_beta`	`str`	Link of auxiliary parameter beta if being predicted.	`'log'`
`link_zi`	`str`	Link of auxiliary parameter zi if being predicted.	`'logit'`
`link_hu`	`str`	Link of auxiliary parameter hu if being predicted.	`'logit'`
`link_zoi`	`str`	Link of auxiliary parameter zoi if being predicted.	`'logit'`
`link_coi`	`str`	Link of auxiliary parameter coi if being predicted.	`'logit'`
`link_disc`	`str`	Link of auxiliary parameter disc if being predicted.	`'log'`
`link_bs`	`str`	Link of auxiliary parameter bs if being predicted.	`'log'`
`link_ndt`	`str`	Link of auxiliary parameter ndt if being predicted.	`'log'`
`link_bias`	`str`	Link of auxiliary parameter bias if being predicted.	`'logit'`
`link_xi`	`str`	Link of auxiliary parameter xi if being predicted.	`'log1p'`
`link_alpha`	`str`	Link of auxiliary parameter alpha if being predicted.	`'identity'`
`link_quantile`	`str`	Link of auxiliary parameter quantile if being predicted.	`'logit'`
`threshold`	`str`	A character string indicating the type of thresholds (i.e. intercepts) used in an ordinal model. "flexible" provides the standard unstructured thresholds, "equidistant" restricts the distance between consecutive thresholds to the same value, and "sum_to_zero" ensures the thresholds sum to zero.	`'flexible'`
`refcat`	`str \| None`	Optional name of the reference response category used in categorical, multinomial, dirichlet and logistic_normal models. If NULL (the default), the first category is used as the reference. If NA, all categories will be predicted, which requires strong priors or carefully specified predictor terms in order to lead to an identified model.	`None`

`family(fit, **kwargs)` ¶

Extract family object from a fitted model.

Parameters:

Name	Type	Description	Default
`fit`	`FitResult or ListSexpVector`	Fitted brms model	required

`student(link='identity', link_sigma='log', link_nu='logm1', **kwargs)` ¶

Student's t distribution for robust regression.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'identity'`
`link_sigma`	`str`	Link function for sigma parameter	`'log'`
`link_nu`	`str`	Link function for degrees of freedom parameter	`'logm1'`

`bernoulli(link='logit', **kwargs)` ¶

Bernoulli distribution for binary 0/1 outcomes.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the probability parameter	`'logit'`

`beta_binomial(link='logit', link_phi='log', **kwargs)` ¶

Beta-binomial distribution for overdispersed binomial data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the probability parameter	`'logit'`
`link_phi`	`str`	Link function for the precision parameter	`'log'`

`negbinomial(link='log', link_shape='log', **kwargs)` ¶

Negative binomial distribution for overdispersed count data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'log'`
`link_shape`	`str`	Link function for the shape parameter	`'log'`

`geometric(link='log', **kwargs)` ¶

Geometric distribution for count data (negative binomial with shape=1).

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'log'`

`lognormal(link='identity', link_sigma='log', **kwargs)` ¶

Lognormal distribution for positive continuous data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean on log scale	`'identity'`
`link_sigma`	`str`	Link function for sigma parameter	`'log'`

`shifted_lognormal(link='identity', link_sigma='log', link_ndt='log', **kwargs)` ¶

Shifted lognormal distribution with non-decision time parameter.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'identity'`
`link_sigma`	`str`	Link function for sigma parameter	`'log'`
`link_ndt`	`str`	Link function for non-decision time parameter	`'log'`

`skew_normal(link='identity', link_sigma='log', link_alpha='identity', **kwargs)` ¶

Skew normal distribution for asymmetric continuous data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'identity'`
`link_sigma`	`str`	Link function for sigma parameter	`'log'`
`link_alpha`	`str`	Link function for skewness parameter	`'identity'`

`exponential(link='log', **kwargs)` ¶

Exponential distribution for time-to-event data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the rate parameter	`'log'`

`weibull(link='log', link_shape='log', **kwargs)` ¶

Weibull distribution for survival and reliability analysis.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the scale parameter	`'log'`
`link_shape`	`str`	Link function for the shape parameter	`'log'`

`frechet(link='log', link_nu='logm1', **kwargs)` ¶

Frechet distribution for extreme value analysis.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the scale parameter	`'log'`
`link_nu`	`str`	Link function for the shape parameter	`'logm1'`

`gen_extreme_value(link='identity', link_sigma='log', link_xi='log1p', **kwargs)` ¶

Generalized extreme value distribution for extreme events.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the location parameter	`'identity'`
`link_sigma`	`str`	Link function for the scale parameter	`'log'`
`link_xi`	`str`	Link function for the shape parameter	`'log1p'`

`exgaussian(link='identity', link_sigma='log', link_beta='log', **kwargs)` ¶

Ex-Gaussian distribution for reaction time data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'identity'`
`link_sigma`	`str`	Link function for Gaussian SD parameter	`'log'`
`link_beta`	`str`	Link function for exponential rate parameter	`'log'`

`wiener(link='identity', link_bs='log', link_ndt='log', link_bias='logit', **kwargs)` ¶

Wiener diffusion model for two-choice reaction time data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for drift rate	`'identity'`
`link_bs`	`str`	Link function for boundary separation	`'log'`
`link_ndt`	`str`	Link function for non-decision time	`'log'`
`link_bias`	`str`	Link function for initial bias	`'logit'`

`Beta(link='logit', link_phi='log', **kwargs)` ¶

Beta distribution for data between 0 and 1.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'logit'`
`link_phi`	`str`	Link function for the precision parameter	`'log'`

`xbeta(link='logit', link_phi='log', link_kappa='log', **kwargs)` ¶

Extended beta distribution with additional shape parameter.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'logit'`
`link_phi`	`str`	Link function for precision parameter	`'log'`
`link_kappa`	`str`	Link function for kappa shape parameter	`'log'`

`dirichlet(link='logit', link_phi='log', refcat=None, **kwargs)` ¶

Dirichlet distribution for compositional data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'logit'`
`link_phi`	`str`	Link function for the precision parameter	`'log'`
`refcat`	`str`	Reference category	`None`

`logistic_normal(link='identity', link_sigma='log', refcat=None, **kwargs)` ¶

Logistic-normal distribution for compositional data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'identity'`
`link_sigma`	`str`	Link function for sigma parameter	`'log'`
`refcat`	`str`	Reference category	`None`

`von_mises(link='tan_half', link_kappa='log', **kwargs)` ¶

Von Mises distribution for circular/directional data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean direction	`'tan_half'`
`link_kappa`	`str`	Link function for concentration parameter	`'log'`

`asym_laplace(link='identity', link_sigma='log', link_quantile='logit', **kwargs)` ¶

Asymmetric Laplace distribution for quantile regression.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the location	`'identity'`
`link_sigma`	`str`	Link function for sigma parameter	`'log'`
`link_quantile`	`str`	Link function for the quantile parameter	`'logit'`

`cox(link='log', **kwargs)` ¶

Cox proportional hazards model for survival data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the hazard rate	`'log'`

`hurdle_poisson(link='log', link_hu='logit', **kwargs)` ¶

Hurdle Poisson distribution for zero-inflated count data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'log'`
`link_hu`	`str`	Link function for hurdle parameter	`'logit'`

`hurdle_negbinomial(link='log', link_shape='log', link_hu='logit', **kwargs)` ¶

Hurdle negative binomial for overdispersed zero-inflated count data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'log'`
`link_shape`	`str`	Link function for shape parameter	`'log'`
`link_hu`	`str`	Link function for hurdle parameter	`'logit'`

`hurdle_gamma(link='log', link_shape='log', link_hu='logit', **kwargs)` ¶

Hurdle Gamma distribution for zero-inflated positive continuous data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'log'`
`link_shape`	`str`	Link function for shape parameter	`'log'`
`link_hu`	`str`	Link function for hurdle parameter	`'logit'`

`hurdle_lognormal(link='identity', link_sigma='log', link_hu='logit', **kwargs)` ¶

Hurdle lognormal for zero-inflated positive continuous data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'identity'`
`link_sigma`	`str`	Link function for sigma parameter	`'log'`
`link_hu`	`str`	Link function for hurdle parameter	`'logit'`

`hurdle_cumulative(link='logit', link_hu='logit', link_disc='log', threshold='flexible', **kwargs)` ¶

Hurdle cumulative for zero-inflated ordinal data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the ordinal response	`'logit'`
`link_hu`	`str`	Link function for hurdle parameter	`'logit'`
`link_disc`	`str`	Link function for discrimination parameter	`'log'`
`threshold`	`str`	Type of threshold structure	`'flexible'`

`zero_inflated_beta(link='logit', link_phi='log', link_zi='logit', **kwargs)` ¶

Zero-inflated beta for data between 0 and 1 with excess zeros.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'logit'`
`link_phi`	`str`	Link function for precision parameter	`'log'`
`link_zi`	`str`	Link function for zero-inflation parameter	`'logit'`

`zero_one_inflated_beta(link='logit', link_phi='log', link_zoi='logit', link_coi='logit', **kwargs)` ¶

Zero-one-inflated beta for data between 0 and 1 with excess zeros and ones.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'logit'`
`link_phi`	`str`	Link function for precision parameter	`'log'`
`link_zoi`	`str`	Link function for zero-or-one inflation parameter	`'logit'`
`link_coi`	`str`	Link function for conditional one inflation parameter	`'logit'`

`zero_inflated_poisson(link='log', link_zi='logit', **kwargs)` ¶

Zero-inflated Poisson for count data with excess zeros.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'log'`
`link_zi`	`str`	Link function for zero-inflation parameter	`'logit'`

`zero_inflated_negbinomial(link='log', link_shape='log', link_zi='logit', **kwargs)` ¶

Zero-inflated negative binomial for overdispersed count data with excess zeros.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'log'`
`link_shape`	`str`	Link function for shape parameter	`'log'`
`link_zi`	`str`	Link function for zero-inflation parameter	`'logit'`

`zero_inflated_binomial(link='logit', link_zi='logit', **kwargs)` ¶

Zero-inflated binomial for binary count data with excess zeros.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for probability parameter	`'logit'`
`link_zi`	`str`	Link function for zero-inflation parameter	`'logit'`

`zero_inflated_beta_binomial(link='logit', link_phi='log', link_zi='logit', **kwargs)` ¶

Zero-inflated beta-binomial for overdispersed binomial data with excess zeros.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for probability parameter	`'logit'`
`link_phi`	`str`	Link function for precision parameter	`'log'`
`link_zi`	`str`	Link function for zero-inflation parameter	`'logit'`

`categorical(link='logit', refcat=None, **kwargs)` ¶

Categorical distribution for unordered multi-category outcomes.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for category probabilities	`'logit'`
`refcat`	`str`	Reference category	`None`

`multinomial(link='logit', refcat=None, **kwargs)` ¶

Multinomial distribution for count data across multiple categories.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for category probabilities	`'logit'`
`refcat`	`str`	Reference category	`None`

`dirichlet_multinomial(link='logit', link_phi='log', refcat=None, **kwargs)` ¶

Dirichlet-multinomial for overdispersed categorical count data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for category probabilities	`'logit'`
`link_phi`	`str`	Link function for precision parameter	`'log'`
`refcat`	`str`	Reference category	`None`

`cumulative(link='logit', link_disc='log', threshold='flexible', **kwargs)` ¶

Cumulative (proportional odds) model for ordinal outcomes.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for cumulative probabilities	`'logit'`
`link_disc`	`str`	Link function for discrimination parameter	`'log'`
`threshold`	`str`	Type of threshold structure	`'flexible'`

`sratio(link='logit', link_disc='log', threshold='flexible', **kwargs)` ¶

Sequential (stopping) ratio model for ordinal outcomes.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for sequential ratios	`'logit'`
`link_disc`	`str`	Link function for discrimination parameter	`'log'`
`threshold`	`str`	Type of threshold structure	`'flexible'`

`cratio(link='logit', link_disc='log', threshold='flexible', **kwargs)` ¶

Continuation ratio model for ordinal outcomes.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for continuation ratios	`'logit'`
`link_disc`	`str`	Link function for discrimination parameter	`'log'`
`threshold`	`str`	Type of threshold structure	`'flexible'`

`acat(link='logit', link_disc='log', threshold='flexible', **kwargs)` ¶

Adjacent category model for ordinal outcomes.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for adjacent category ratios	`'logit'`
`link_disc`	`str`	Link function for discrimination parameter	`'log'`
`threshold`	`str`	Type of threshold structure	`'flexible'`

`gaussian(link='identity', link_sigma='log', **kwargs)` ¶

Gaussian (normal) distribution for continuous data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'identity'`
`link_sigma`	`str`	Link function for the standard deviation	`'log'`

`poisson(link='log', **kwargs)` ¶

Poisson distribution for count data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the rate parameter	`'log'`

`binomial(link='logit', **kwargs)` ¶

Binomial distribution for binary count data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the probability parameter	`'logit'`

`Gamma(link='log', link_shape='log', **kwargs)` ¶

Gamma distribution for positive continuous data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'log'`
`link_shape`	`str`	Link function for the shape parameter	`'log'`

`inverse_gaussian(link='1/mu^2', link_shape='log', **kwargs)` ¶

Inverse Gaussian distribution for positive continuous data.

Parameters:

Name	Type	Description	Default
`link`	`str`	Link function for the mean	`'1/mu^2'`
`link_shape`	`str`	Link function for the shape parameter	`'log'`

`get_brms_version()` ¶

Get installed brms R package version.

Returns:

Type	Description
`str`	Version object or None

Raises:

Type	Description
`ImportError`	If brms is not installed

Examples:

from brmspy import brms
version = brms.get_brms_version()
print(f"brms version: {version}")

`find_local_runtime()` ¶

Find an installed runtime matching the current system fingerprint.

Uses system_fingerprint() to compute the current system identity and searches the local runtime store for a matching runtime directory.

Returns:

Type	Description
`Path or None`	Path to the matching runtime root directory if found, otherwise `None`.

Notes

This function is a pure lookup: it does not install, activate, or modify any runtime state.

`get_active_runtime()` ¶

Get path to currently active prebuilt runtime.

Returns CONFIGURED runtime, not whether it is loaded.

Returns:

Type	Description
`Path or None`	Path to active runtime directory, or None if not configured

Notes

Returns None if: - No runtime configured in config file - Config file doesn't exist - Config file is corrupted

Examples:

from brmspy import get_active_runtime

runtime_path = get_active_runtime()
if runtime_path and runtime_path.exists():
    print(f"Active runtime: {runtime_path}")
else:
    print("No active runtime configured")

`status()` ¶

Query current runtime status without side effects.

Returns:

Type	Description
`RuntimeStatus`	Dataclass with comprehensive state information including: Active runtime path and activation state System fingerprint and toolchain info Prebuilt compatibility and availability Installed brms/cmdstanr/rstan versions

Main Module

Classes¶

IDResult dataclass ¶

LooResult dataclass ¶

Functions¶

__repr__() ¶

LooCompareResult dataclass ¶

IDPosterior ¶

IDPosteriorPredictive ¶

IDPredictions ¶

IDLogLikelihoodInsample ¶

IDLogLikelihoodOutsample ¶

IDObservedData ¶

IDConstantData ¶

IDPredictionsConstantData ¶

RListVectorExtension dataclass ¶

IDBrm ¶

PriorSpec dataclass ¶

Functions¶

to_brms_kwargs() ¶

FormulaConstruct dataclass ¶

Functions¶

__add__(other) ¶

__radd__(other) ¶

iter_summands() ¶

__iter__() ¶

iterate() ¶

FormulaPart dataclass ¶

Functions¶

__post_init__() ¶

__str__() ¶

Functions¶

log(*msg, method_name=None, level=logging.INFO) ¶

log_warning(msg, method_name=None) ¶

get_brms_data(dataset_name, **kwargs) ¶

read_rds_fit(file, **kwargs) ¶

read_rds_raw(file, **kwargs) ¶

save_rds(object, file, **kwargs) ¶

get_data(dataset_name, **kwargs) ¶

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

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

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

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

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

summary(model, **kwargs) ¶

fixef(object, summary=True, robust=False, probs=(0.025, 0.975), pars=None, **kwargs) ¶

ranef(object, summary=True, robust=False, probs=(0.025, 0.975), pars=None, groups=None, **kwargs) ¶

posterior_summary(object, variable=None, probs=(0.025, 0.975), robust=False, **kwargs) ¶

prior_summary(object, all=True, **kwargs) ¶

validate_newdata(newdata, object, re_formula=None, allow_new_levels=False, newdata2=None, resp=None, check_response=True, incl_autocor=True, group_vars=None, req_vars=None, **kwargs) ¶

call(function, *args, **kwargs) ¶

bf(*formulas, **formula_args) ¶

lf(*formulas, flist=None, dpar=None, resp=None, center=None, cmc=None, sparse=None, decomp=None) ¶

nlf(*formulas, flist=None, dpar=None, resp=None, loop=None) ¶

acformula(autocor, resp=None) ¶

set_rescor(rescor=True) ¶

set_mecor(mecor=True) ¶

set_nl(dpar=None, resp=None) ¶

formula(*formulas, **formula_args) ¶

posterior_epred(model, newdata=None, **kwargs) ¶

posterior_linpred(model, newdata=None, **kwargs) ¶

posterior_predict(model, newdata=None, **kwargs) ¶

log_lik(model, newdata=None, **kwargs) ¶

posterior(model, **kwargs) ¶

make_stancode(formula, data, priors=None, family='poisson', sample_prior='no', formula_args=None) ¶

family(fit, **kwargs) ¶

student(link='identity', link_sigma='log', link_nu='logm1', **kwargs) ¶

bernoulli(link='logit', **kwargs) ¶

beta_binomial(link='logit', link_phi='log', **kwargs) ¶

negbinomial(link='log', link_shape='log', **kwargs) ¶

geometric(link='log', **kwargs) ¶

lognormal(link='identity', link_sigma='log', **kwargs) ¶

shifted_lognormal(link='identity', link_sigma='log', link_ndt='log', **kwargs) ¶

skew_normal(link='identity', link_sigma='log', link_alpha='identity', **kwargs) ¶

exponential(link='log', **kwargs) ¶

weibull(link='log', link_shape='log', **kwargs) ¶

frechet(link='log', link_nu='logm1', **kwargs) ¶

gen_extreme_value(link='identity', link_sigma='log', link_xi='log1p', **kwargs) ¶

exgaussian(link='identity', link_sigma='log', link_beta='log', **kwargs) ¶

wiener(link='identity', link_bs='log', link_ndt='log', link_bias='logit', **kwargs) ¶

`IDResult` `dataclass` ¶

`LooResult` `dataclass` ¶

`repr()` ¶

`LooCompareResult` `dataclass` ¶

`IDPosterior` ¶

`IDPosteriorPredictive` ¶

`IDPredictions` ¶

`IDLogLikelihoodInsample` ¶

`IDLogLikelihoodOutsample` ¶

`IDObservedData` ¶

`IDConstantData` ¶

`IDPredictionsConstantData` ¶

`RListVectorExtension` `dataclass` ¶

`IDBrm` ¶

`PriorSpec` `dataclass` ¶

`to_brms_kwargs()` ¶

`FormulaConstruct` `dataclass` ¶

`add(other)` ¶

`radd(other)` ¶

`iter_summands()` ¶

`iter()` ¶

`iterate()` ¶

`FormulaPart` `dataclass` ¶

`__post_init__()` ¶

`str()` ¶

`log(*msg, method_name=None, level=logging.INFO)` ¶

`log_warning(msg, method_name=None)` ¶

`get_brms_data(dataset_name, **kwargs)` ¶

`read_rds_fit(file, **kwargs)` ¶

`read_rds_raw(file, **kwargs)` ¶

`save_rds(object, file, **kwargs)` ¶

`get_data(dataset_name, **kwargs)` ¶

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

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

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

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

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

`summary(model, **kwargs)` ¶

`fixef(object, summary=True, robust=False, probs=(0.025, 0.975), pars=None, **kwargs)` ¶

`ranef(object, summary=True, robust=False, probs=(0.025, 0.975), pars=None, groups=None, **kwargs)` ¶

`posterior_summary(object, variable=None, probs=(0.025, 0.975), robust=False, **kwargs)` ¶

`prior_summary(object, all=True, **kwargs)` ¶

`validate_newdata(newdata, object, re_formula=None, allow_new_levels=False, newdata2=None, resp=None, check_response=True, incl_autocor=True, group_vars=None, req_vars=None, **kwargs)` ¶

`call(function, *args, **kwargs)` ¶

`bf(*formulas, **formula_args)` ¶

`lf(*formulas, flist=None, dpar=None, resp=None, center=None, cmc=None, sparse=None, decomp=None)` ¶

`nlf(*formulas, flist=None, dpar=None, resp=None, loop=None)` ¶

`acformula(autocor, resp=None)` ¶

`set_rescor(rescor=True)` ¶

`set_mecor(mecor=True)` ¶

`set_nl(dpar=None, resp=None)` ¶

`formula(*formulas, **formula_args)` ¶

`posterior_epred(model, newdata=None, **kwargs)` ¶

`posterior_linpred(model, newdata=None, **kwargs)` ¶

`posterior_predict(model, newdata=None, **kwargs)` ¶

`log_lik(model, newdata=None, **kwargs)` ¶

`posterior(model, **kwargs)` ¶

`make_stancode(formula, data, priors=None, family='poisson', sample_prior='no', formula_args=None)` ¶

`family(fit, **kwargs)` ¶

`student(link='identity', link_sigma='log', link_nu='logm1', **kwargs)` ¶

`bernoulli(link='logit', **kwargs)` ¶

`beta_binomial(link='logit', link_phi='log', **kwargs)` ¶

`negbinomial(link='log', link_shape='log', **kwargs)` ¶

`geometric(link='log', **kwargs)` ¶

`lognormal(link='identity', link_sigma='log', **kwargs)` ¶

`shifted_lognormal(link='identity', link_sigma='log', link_ndt='log', **kwargs)` ¶

`skew_normal(link='identity', link_sigma='log', link_alpha='identity', **kwargs)` ¶

`exponential(link='log', **kwargs)` ¶

`weibull(link='log', link_shape='log', **kwargs)` ¶

`frechet(link='log', link_nu='logm1', **kwargs)` ¶

`gen_extreme_value(link='identity', link_sigma='log', link_xi='log1p', **kwargs)` ¶

`exgaussian(link='identity', link_sigma='log', link_beta='log', **kwargs)` ¶

`wiener(link='identity', link_bs='log', link_ndt='log', link_bias='logit', **kwargs)` ¶

`Beta(link='logit', link_phi='log', **kwargs)` ¶

`xbeta(link='logit', link_phi='log', link_kappa='log', **kwargs)` ¶

`dirichlet(link='logit', link_phi='log', refcat=None, **kwargs)` ¶

`logistic_normal(link='identity', link_sigma='log', refcat=None, **kwargs)` ¶

`von_mises(link='tan_half', link_kappa='log', **kwargs)` ¶

`asym_laplace(link='identity', link_sigma='log', link_quantile='logit', **kwargs)` ¶

`cox(link='log', **kwargs)` ¶