brmspy 0.1.4

Python-first access to R's brms with proper parameter names, ArviZ support, and cmdstanr performance

brmspy

Python-first access to R's brms with proper parameter names, ArviZ support, and cmdstanr performance. The easiest way to run brms models from Python.

Overview

This is an early development version of the library, use with caution.

brmspy provides Python access to brms (Bayesian Regression Models using Stan) with proper parameter naming and seamless arviz integration. Uses brms with cmdstanr backend. Arviz is required at the moment, numpy-only mode is coming.

Installation

pip install brmspy

First-time setup (installs brms, cmdstanr, and CmdStan in R):

from brmspy import brms
brms.install_brms()

Quick Start

from brmspy import brms
import arviz as az

# Load data
epilepsy = brms.get_brms_data("epilepsy")

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

# Analyze
az.summary(model.idata)
az.plot_posterior(model.idata)

Key Features

• Proper parameter names: Returns b_Intercept, b_zAge, sd_patient__Intercept instead of generic names like b_dim_0
• arviz integration: Returns arviz.InferenceData by default for Python workflow
• brms formula syntax: Full support for brms formula interface including random effects
• Dual access: Results include both .idata (arviz) and .r (brmsfit) attributes

API Reference

Setup Functions

• brms.install_brms() - Install brms, cmdstanr, and CmdStan
• brms.get_brms_version() - Get installed brms version

Data Functions

• brms.get_brms_data() - Load example datasets from brms

Model Functions

• brms.fit() - Fit Bayesian regression model
• brms.summary() - Generate summary statistics as DataFrame
• brms.get_stan_code() - Generate Stan code for model

Prediction Functions

• brms.posterior_epred() - Expected value predictions (without noise)
• brms.posterior_predict() - Posterior predictive samples (with noise)
• brms.posterior_linpred() - Linear predictor values
• brms.log_lik() - Log-likelihood values

Usage

Basic Model

from brmspy import brms

kidney = brms.get_brms_data("kidney")

model = brms.fit(
    formula="time ~ age + disease",
    data=kidney,
    family="gaussian",
    chains=4,
    iter=2000
)

With Priors

model = brms.fit(
    formula="count ~ zAge + (1|patient)",
    data=epilepsy,
    family="poisson",
    priors=[
        ("normal(0, 0.5)", "b"),
        ("cauchy(0, 1)", "sd")
    ],
    chains=4
)

Model Summary

from brmspy import summary

# Get summary statistics as DataFrame
summary_df = summary(model)
print(summary_df)

Predictions

# Expected value (without noise)
epred = brms.posterior_epred(model, newdata=new_data)

# Posterior predictive (with noise)
ypred = brms.posterior_predict(model, newdata=new_data)

# Linear predictor
linpred = brms.posterior_linpred(model, newdata=new_data)

# Log likelihood
loglik = brms.log_lik(model, newdata=new_data)

Access Both Python and R Objects

model = brms.fit(formula="y ~ x", data=data, chains=4)

# Python workflow with arviz
az.summary(model.idata)
az.plot_trace(model.idata)

# R workflow (if needed)
import rpy2.robjects as ro
ro.r('summary')(model.r)

Sampling Parameters

model = brms.fit(
    formula="y ~ x + (1|group)",
    data=data,
    iter=2000,      # Total iterations per chain
    warmup=1000,    # Warmup iterations
    chains=4,       # Number of chains
    cores=4,        # Parallel cores
    thin=1,         # Thinning
    seed=123        # Random seed
)

Requirements

Python: 3.10-3.14

R packages (auto-installed via brms.install_brms()):

• brms >= 2.20.0
• cmdstanr
• posterior

Python dependencies:

• rpy2 >= 3.5.0
• pandas >= 1.3.0
• numpy >= 1.20.0
• arviz (optional, for InferenceData)

Development

git clone https://github.com/kaitumisuuringute-keskus/brmspy.git
cd brmspy
./init-venv.sh
pytest tests/ -v

Architecture

brmspy uses:

• brms::brm() with cmdstanr backend for fitting (ensures proper parameter naming)
• posterior R package for conversion to draws format
• arviz for Python-native analysis and visualization
• rpy2 for Python-R communication

Previous versions used CmdStanPy directly, which resulted in generic parameter names. Current version calls brms directly to preserve brms' parameter renaming logic.

License

Apache License 2.0

Credits

• Original concept: Adam Haber
• Current maintainer: Remi Sebastian Kits
• Built on brms by Paul-Christian Bürkner