Stan
Probabilistic programming with full Bayesian inference and Hamiltonian Monte Carlo sampling.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
UnverifiedOverview
What is Stan?
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.
Key differentiator
“Stan stands out for its robust implementation of Bayesian inference and Hamiltonian Monte Carlo, offering unparalleled flexibility and efficiency in statistical modeling.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
Understanding Bayesian inference and implementing complex statistical models requires a strong background in statistics.
Primary interfaces are through C++, R, Python (via PyStan), and Stan itself; other languages have limited or no direct support.
Installation requires setting up compilers and dependencies, which can be challenging on some operating systems.
Hamiltonian Monte Carlo sampling can become computationally expensive as the complexity of the model increases or when dealing with large datasets.
Fit analysis
Who is it for?
✓ Best for
Research teams needing precise Bayesian inference for complex models
Academics working on advanced statistical methods and simulations
Developers building custom statistical tools requiring high performance
✕ Not a fit for
Projects with strict real-time requirements due to computational intensity
Teams preferring a graphical user interface over programming-based solutions
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Works well with
Integrations
Next step
Get Started with Stan
Step-by-step setup guide with code examples and common gotchas.