Stan vs Manifold: What are the differences?

Stan: A Probabilistic Programming Language. A state-of-the-art platform for statistical modeling and high-performance statistical computation. Used for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences, engineering, and business; Manifold: A model-agnostic visual debugging tool for machine learning. Understanding ML model performance and behavior is a non-trivial process, given the intrisic opacity of ML algorithms. Performance summary statistics such as AUC, RMSE, and others are not instructive enough for identifying what went wrong with a model or how to improve it. As a visual analytics tool, Manifold allows ML practitioners to look beyond overall summary metrics to detect which subset of data a model is inaccurately predicting.

Stan and Manifold belong to "Machine Learning Tools" category of the tech stack.

Stan and Manifold are both open source tools. Stan with 1.75K GitHub stars and 288 forks on GitHub appears to be more popular than Manifold with 778 GitHub stars and 58 GitHub forks.