Nimble vs Stan: What are the differences?

Nimble and Stan are two popular tools in the field of probabilistic programming that have their own unique features and functions. Below are some key differences between Nimble and Stan:

1. **Model Specification**: Nimble allows users to specify models in a more flexible and intuitive manner compared to Stan. It offers a simplified syntax for defining probabilistic models, making it easier for users to work with complex models efficiently.
   
2. **Compilation and Inference**: Stan uses a more optimized compilation and inference engine, resulting in faster computation times for large and complex models. It is highly efficient in handling a wide range of statistical models and performing Bayesian inference.
   
3. **Language Syntax**: Nimble is based on the R programming language, which makes it easier for R users to integrate their existing code with Nimble models. On the contrary, Stan uses its own modeling language, which may require users to learn a new syntax.
   
4. **Visualization Tools**: Stan provides more advanced visualization tools compared to Nimble. It offers built-in functions for diagnostic plots, posterior inference, and model comparison, facilitating a better understanding of the model results.
   
5. **Extensibility**: Nimble allows users to extend the functionalities of the software through custom functions and packages, enabling them to tailor the tool to their specific needs. In contrast, Stan has a more rigid structure, limiting the extent to which users can customize the tool.
   
6. **Community Support**: Stan has a larger and more active community compared to Nimble, providing users with a wider range of resources, tutorials, and forums for troubleshooting and collaboration.

In Summary, Nimble and Stan differ in terms of model specification flexibility, computational efficiency, language syntax, visualization tools, extensibility, and community support, catering to different user needs and preferences in probabilistic programming.