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Safely-rounded Confidence Sequence Method
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Safely-rounded Confidence Sequence Method

An implementation of Ding, Gandy, and Hahn's Confidence Sequence Method for dynamic termination of Binomial tests on Monte Carlo simulations, as well as code to compute Bayesian credible intervals on the underlying success rate.

The code in C, Common Lisp, and Python should give bitwise identical results, and the results should always be safely (conservatively) rounded; the implementation of CSM in floating point should not cause any false positive. N.B., The reason I care about round-off errors isn't that I expect them to make a big difference, but simply that I want peace of mind, and not have to wonder if weird numerical issues are causing whatever my system reports.

See this blog post for more information.

I'd particularly like to receive suggestions or pull requests to improve the usability of the tools. I'm thinking improvements to the report format, graphical reports, and CLIs tools that are easier to insert in testing pipelines or in ad hoc shell scripts.

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