R vs Stan: What are the differences?

Introduction:

R and Stan are both programming languages commonly used in statistical analysis and modeling. While they share some similarities, there are several key differences between the two.

1. Syntax and Coding Style: R uses a syntax that is similar to other programming languages, making it relatively easy to learn for those with programming experience. It typically consists of writing code in a script or through interactive command-line sessions. Stan, on the other hand, uses its own specialized syntax, which can be more complex and takes some time to master. Stan code is written in a separate file and compiled before it can be executed.

2. Computational Efficiency: R is an interpreted language, which means that code is executed sequentially and can be slower for computationally intensive tasks. Stan, in contrast, uses a compiled approach, which allows for more efficient execution, especially for complex statistical models and simulations. This makes Stan well-suited for handling large datasets and performing Bayesian analysis.

3. Model Specification: In R, model specification is typically done using formulas and built-in functions, which provide a high-level and convenient way to express relationships between variables. Stan, on the other hand, requires explicit and detailed specification of the probability models. This includes explicitly defining and parameterizing the priors, likelihood, and any constraints on the parameters.

4. Sampling Algorithms: Both R and Stan employ various sampling algorithms for inference, such as Markov Chain Monte Carlo (MCMC) methods. However, Stan offers more advanced and efficient sampling algorithms, such as Hamiltonian Monte Carlo (HMC), which can provide faster convergence and better exploration of the posterior distribution. In addition, Stan provides automatic differentiation, allowing for more flexibility in defining complex models.

5. Flexibility and Extensibility: R is a highly flexible and extensible language, with a vast ecosystem of packages for data manipulation, visualization, and statistical analysis. It is widely used across different domains and has a large community of developers. Stan, while not as extensive as R, provides a flexible framework for Bayesian analysis and has its own set of packages for modeling and inference. It also supports the incorporation of user-defined functions and modules.

6. Debugging and Error Handling: R has well-developed debugging tools and error handling mechanisms that make it relatively easy to identify and fix issues in code. Stan, being a compiled language, can be more challenging to debug. However, Stan provides a range of diagnostic tools and error messages that can help pinpoint potential problems in model specification or inference.

In Summary, R and Stan differ in terms of syntax and coding style, computational efficiency, model specification, sampling algorithms, flexibility and extensibility, as well as debugging and error handling capabilities.