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.

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

Github repo and issues

Installation¶

R Configuration¶

R>=4 is required before installing brmspy.

On linux and macos you may need to manually point your environment to the R installation.

Run the following in your terminal:

# Set R_HOME and add lib directory to LD_LIBRARY_PATH
export R_HOME=$(R RHOME)
export LD_LIBRARY_PATH="${R_HOME}/lib:${LD_LIBRARY_PATH}"

Python¶

pip install brmspy

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

from brmspy import brms
brms.install_brms() # requires R to be installed already

Prebuilt Runtimes (Optional)¶

For faster installation (~20-60 seconds vs 20-30 minutes), use prebuilt runtime bundles:

from brmspy import brms
brms.install_brms(use_prebuilt=True)

Windows RTools¶

In case you don't have RTools installed, you can use the flag install_rtools = True. This is disabled by default, because the flag runs the full rtools installer and modifies system path. Use with caution!

from brmspy import brms
brms.install_brms(
    use_prebuilt=True,
    install_rtools=True # works for both prebuilt and compiled binaries.
)

System Requirements¶

Linux (x86_64): - glibc >= 2.27 (Ubuntu 18.04+, Debian 10+, RHEL 8+) - g++ >= 9.0 - R >= 4.3

macOS (Intel & Apple Silicon): - Xcode Command Line Tools: xcode-select --install - clang >= 11.0 - R >= 4.2

Windows (x86_64): - Rtools 4.0+ with MinGW toolchain - g++ >= 9.0 - R >= 4.5

Download Rtools from: https://cran.r-project.org/bin/windows/Rtools/

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
No reimplementation: Delegates all modeling logic to real brms. No Python-side reimplementation, no divergence from native behavior
Prebuilt Binaries: Fast installation with precompiled runtimes (50x faster, ~25 seconds on Google Colab)
Stays true to brms: Function names, parameters, and returned objects are designed to be as close as possible to brms
Composable formula DSL: Build multivariate, non-linear, and distributional formulas by simply adding components together, identical to brms

Examples¶

1. Quick Start¶

Basic Bayesian regression with ArviZ diagnostics:

from brmspy import brms
import arviz as az

# Fit Poisson model with random effects
epilepsy = brms.get_brms_data("epilepsy")
model = brms.brm("count ~ zAge + (1|patient)", data=epilepsy, family="poisson")

# Proper parameter names automatically!
print(az.summary(model.idata))
#                  mean     sd  hdi_3%  hdi_97%  ...  r_hat
# b_Intercept     1.234  0.123   1.012    1.456  ...   1.00
# b_zAge          0.567  0.089   0.398    0.732  ...   1.00
# sd_patient__... 0.345  0.067   0.223    0.467  ...   1.00

2. Multivariate Models (Python vs R)¶

Model multiple responses simultaneously with seamless ArviZ integration:

Python (brmspy) R (brms)

from brmspy import brms, bf, set_rescor
import arviz as az

# Fit multivariate model
mv = brms.brm(
    bf("mvbind(tarsus, back) ~ sex + (1|p|fosternest)")
    + set_rescor(True),
    data=btdata
)

# ArviZ just works!
az.loo(mv.idata, var_name="tarsus")
az.loo(mv.idata, var_name="back")
az.plot_ppc(mv.idata, var_names=["tarsus"])

library(brms)
library(loo)

# Fit multivariate model
fit <- brm(
  bf(mvbind(tarsus, back) ~ sex + (1|p|fosternest))
  + set_rescor(TRUE),
  data = BTdata
)

# Separate LOO for each response
loo_tarsus <- loo(fit, resp = "tarsus")
loo_back <- loo(fit, resp = "back")

3. Distributional Regression¶

Model heteroscedasticity (variance depends on predictors):

from brmspy import bf

# Model both mean AND variance
model = brms.brm(
    bf("reaction ~ days", sigma = "~ days"),  # sigma varies with days!
    data=sleep_data,
    family="gaussian"
)

# Extract distributional parameters
print(model.idata.posterior.data_vars)
# b_Intercept, b_days, b_sigma_Intercept, b_sigma_days, ...

4. Complete Diagnostic Workflow with ArviZ¶

Full model checking in ~10 lines:

from brmspy import brms
import arviz as az

model = brms.brm("count ~ zAge * Trt + (1|patient)", data=epilepsy, family="poisson")

# Check convergence
assert az.rhat(model.idata).max() < 1.01, "Convergence issues!"
assert az.ess(model.idata).min() > 400, "Low effective sample size!"

# Posterior predictive check
az.plot_ppc(model.idata, num_pp_samples=100)

# Model comparison
model2 = brms.brm("count ~ zAge + Trt + (1|patient)", data=epilepsy, family="poisson")
comparison = az.compare({"interaction": model.idata, "additive": model2.idata})
print(comparison)
#              rank  loo    p_loo  d_loo  weight
# interaction     0 -456.2   12.3    0.0    0.89
# additive        1 -461.5   10.8    5.3    0.11

5. Advanced Formulas: Splines & Non-linear Effects¶

Smooth non-linear relationships with splines:

from brmspy import brms

# Generalized additive model (GAM) with spline
model = brms.brm(
    "y ~ s(x, bs='cr', k=10) + (1 + x | group)",
    data=data,
    family="gaussian"
)

# Polynomial regression
poly_model = brms.brm(
    "y ~ poly(x, 3) + (1|group)",
    data=data
)

# Extract and visualize smooth effects
conditional_effects = brms.call("conditional_effects", model, "x")

Additional Features¶

Custom Priors:

from brmspy import prior

model = brms.brm(
    "count ~ zAge + (1|patient)",
    data=epilepsy,
    priors=[
        prior("normal(0, 0.5)", class_="b"),
        prior("exponential(1)", class_="sd", group="patient")
    ],
    family="poisson"
)

Predictions:

import pandas as pd

new_data = pd.DataFrame({"zAge": [-1, 0, 1], "patient": [999, 999, 999]})

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

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

# Access as InferenceData for ArviZ
az.plot_violin(epred.idata)

6. Maximalist Example: Kitchen Sink¶

Everything at once - multivariate responses, different families, distributional parameters, splines, and complete diagnostics:

from brmspy import brms, bf, lf, set_rescor, skew_normal, gaussian
import arviz as az

# Load data
btdata = brms.get_data("BTdata", package="MCMCglmm")

bf_tarsus = (
    bf("tarsus ~ sex + (1|p|fosternest) + (1|q|dam)") +
    lf("sigma ~ 0 + sex") +
    skew_normal()
)

bf_back = (
    bf("back ~ s(hatchdate) + (1|p|fosternest) + (1|q|dam)") +
    gaussian()
)

model = brms.brm(
    bf_tarsus + bf_back + set_rescor(False),
    data=btdata,
    chains=2,
    control={"adapt_delta": 0.95}
)

# ArviZ diagnostics work seamlessly
for response in ["tarsus", "back"]:
    print(f"\n=== {response.upper()} ===")

    # Model comparison
    loo = az.loo(model.idata, var_name=response)
    print(f"LOO: {loo.loo:.1f} ± {loo.loo_se:.1f}")

    # Posterior predictive check
    az.plot_ppc(model.idata, var_names=[response])

    # Parameter summaries
    print(az.summary(
        model.idata,
        var_names=[f"b_{response}"],
        filter_vars="like"
    ))

# Visualize non-linear effect
conditional = brms.call("conditional_effects", model, "hatchdate", resp="back")
# Returns proper pandas DataFrame ready for plotting!

Output shows: - Proper parameter naming: b_tarsus_Intercept, b_tarsus_sex, b_sigma_sex, sd_fosternest__tarsus_Intercept, etc. - Separate posterior predictive for each response - Per-response LOO for model comparison - All parameters accessible via ArviZ

API Reference (partial)¶

brmspy documentation

brms documentation

Setup Functions¶

It is NOT recommended to run installation functions when you have used the session.

install_brms() - Install brms, cmdstanr, and CmdStan from source or runtime
install_runtime() - Install latest runtime for OS
activate_runtime() - Activate existing prebuilt runtime
deactivate_runtime() - Deactivate current runtime - May break on windows.
get_brms_version() - Get installed brms version
find_local_runtime() - checks if a runtime exists locally in standard directory and returns path if it does

Data Functions¶

get_brms_data() - Load example datasets from brms
get_data() - Load example datasets from any package
save_rds() - Save brmsfit or another robject
load_rds_fit() - Load saved brmsfit object as FitResult (with idata)
load_rds_raw() - Load r object

Model Functions¶

bf, lg, nlf, acformula, set_rescor, set_mecor, set_nl - formula functions
brm() - Fit Bayesian regression model
add_criterion - add loo, waic criterions to fit
make_stancode() - Generate Stan code for model

Diagnostics Functions¶

summary() - Comprehensive model summary as SummaryResult dataclass
fixef() - Extract population-level (fixed) effects
ranef() - Extract group-level (random) effects as xarray
posterior_summary() - Summary statistics for all parameters
prior_summary() - Extract prior specifications used in model
validate_newdata() - Validate new data for predictions
For loo, waic etc use arviz!

Prior Functions¶

prior() - Define a prior with same syntax as r-s prior_string
get_prior() - Get pd.DataFrame describing default priors
default_prior() - Get pd.DataFrame describing default priors

Families Functions¶

family() - Get family object of FitResult
brmsfamily() - Construct family object from kwargs
gaussian(), ...bernoulli(), ...beta_binomial(), etc - Wrappers around brmsfamily for faster family object construction

Prediction Functions¶

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

Generic Function Access¶

call() - Call any brms/R function by name with automatic type conversion

Known issues¶

Due to Windows' idiosyncrasies installing existing R packages (or cmdstanr) is NOT guaranteed to succeed in the same session if it has already been used. It is strongly recommended to restart your Python session before doing any installations when you have used it. This also means autoloading previously used prebuilt environment on windows is disabled, call activate() to load existing prebuilt runtime.

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.