PyStan has an interface similar to that of RStan. For an introduction to Stan and RStan see http://mc-stan.org/ and RStan Getting Started.

• Source code repo: https://github.com/ariddell/pystan
• HTML documentation: https://pystan.readthedocs.org
• Issue tracker: https://github.com/ariddell/pystan/issues
• Stan: http://mc-stan.org/

NumPy and Cython (version 0.19.1 or greater) are required.

git clone https://github.com/ariddell/pystan.git
cd pystan
python setup.py install

import pystan
import numpy as np

schools_code = """
data {
    int<lower=0> J; // number of schools
    real y[J]; // estimated treatment effects
    real<lower=0> sigma[J]; // s.e. of effect estimates
}
parameters {
    real mu;
    real<lower=0> tau;
    real eta[J];
}
transformed parameters {
    real theta[J];
    for (j in 1:J)
    theta[j] <- mu + tau * eta[j];
}
model {
    eta ~ normal(0, 1);
    y ~ normal(theta, sigma);
}
"""

schools_dat = {'J': 8,
               'y': [28,  8, -3,  7, -1,  1, 18, 12],
               'sigma': [15, 10, 16, 11,  9, 11, 10, 18]}

fit = pystan.stan(model_code=schools_code, data=schools_dat,
                  iter=1000, chains=4)

eta = fit.extract(permuted=True)['eta']
np.mean(eta, axis=0)