Stan

What it is

Stan is a probabilistic programming language that allows users to implement statistical models using full Bayesian inference. It uses Hamiltonian Monte Carlo for efficient sampling, making it suitable for complex data analysis tasks.

Stan stands out for its robust implementation of Bayesian inference and Hamiltonian Monte Carlo, offering unparalleled flexibility and efficiency in statistical modeling.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Steep learning curve for non-statisticians

   Understanding Bayesian inference and implementing complex statistical models requires a strong background in statistics.

2. 02

   Limited language support beyond C++ and R

   Primary interfaces are through C++, R, Python (via PyStan), and Stan itself; other languages have limited or no direct support.

3. 03

   Complex setup process for new users

   Installation requires setting up compilers and dependencies, which can be challenging on some operating systems.

4. 04

   Performance issues with very large datasets or complex models

   Hamiltonian Monte Carlo sampling can become computationally expensive as the complexity of the model increases or when dealing with large datasets.

What it does well

• Full Bayesian inference with Hamiltonian Monte Carlo sampling

• Support for a wide range of statistical models

• Highly optimized and efficient computation

• Extensive documentation and community support

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.