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Python interface to Stan, a package for Bayesian inference

## Project description

PyStan provides a Python interface to Stan, a package for Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo.

For more information on Stan and its modeling language, see the Stan User’s Guide and Reference Manual at http://mc-stan.org/.

## PyStan3 / Stan3

The development of PyStan3 with updated API can be found under stan-dev/pystan-next

## Detailed Installation Instructions

Detailed installation instructions can be found in the doc/installation_beginner.md file.

## Windows Installation Instructions

Detailed installation instructions for Windows can be found in docs under PyStan on Windows

## Quick Installation (Linux and macOS)

NumPy and Cython (version 0.22 or greater) are required. matplotlib is optional. ArviZ is recommended for visualization and analysis.

PyStan and the required packages may be installed from the Python Package Index using pip.

```pip install pystan
```

Alternatively, if Cython (version 0.22 or greater) and NumPy are already available, PyStan may be installed from source with the following commands

```git clone --recursive https://github.com/stan-dev/pystan.git
cd pystan
python setup.py install
```

To install latest development version user can also use pip

```pip install git+https://github.com/stan-dev/pystan
```

If you encounter an ImportError after compiling from source, try changing out of the source directory before attempting import pystan. On Linux and OS X cd /tmp will work.

## Example

```import pystan
import numpy as np
import matplotlib.pyplot as plt

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]}

sm = pystan.StanModel(model_code=schools_code)
fit = sm.sampling(data=schools_dat, iter=1000, chains=4)

print(fit)

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

# if matplotlib is installed (optional, not required), a visual summary and
# traceplot are available
fit.plot()
plt.show()

# updated traceplot can be plotted with
import arviz as az
az.plot_trace(fit)
```

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