inferentialPL

inferentialPL is a programming language designed for statistical inference. It aims to simplify the process of building and analyzing statistical models, particularly those involving Bayesian inference and causal reasoning. The language provides a high-level syntax that allows users to express complex statistical models in a clear and concise manner.

One of the key features of inferentialPL is its built-in support for common statistical distributions and modeling techniques. This includes capabilities for defining probability distributions, specifying model structures, and performing parameter estimation. The language is designed to be expressive, allowing users to translate their statistical ideas directly into code without needing to manage low-level computational details.

inferentialPL also emphasizes the importance of interpretability and reproducibility in statistical analysis. It aims to generate code that is easy to understand and modify, and provides tools for tracking the provenance of analyses. This is particularly relevant in fields where rigorous statistical methods are crucial, such as machine learning, econometrics, and scientific research. The language's design seeks to bridge the gap between statistical theory and practical implementation.