Here are some software packages that I have worked on.
confseq: Confidence sequences and uniform boundaries
This Python package (maintained by Steve Howard and me) implements several nonasymptotic confidence sequences, including those from Howard et al. (2021), Howard & Ramdas (2022), Waudby-Smith & Ramdas (2020) and Waudby-Smith & Ramdas (2023).
[github · pypi]
drconfseq: Doubly robust confidence sequences for sequential causal inference
This R package implements the asymptotic confidence sequences of Waudby-Smith et al. and specifically confidence sequences for doubly robust treatment effect estimation in observational studies and randomized experiments.
[github]
RiLACS: Risk-limiting election audits via confidence sequences
This Python package implements methods introduced in Waudby-Smith, Stark, and Ramdas (2021) for using confidence sequences and betting martingale-based tests to audit elections, and more specifically, any election that can be reduced to mean testing/estimation via SHANGRLA (Stark, 2020).
[github · pypi]
NPRR: Nonparametric randomized response
This Python package implements our nonparametric generalization of Warner’s randomized response as well as methods for computing differentially private confidence intervals and sequences for means of bounded random variables as in Waudby-Smith, Wu, and Ramdas (2023).
[github · pypi]
Methods that I have developed have also appeared in some other open-source projects (by people that write much better code than me) such as Microsoft’s VowpalWabbit (see this commit) or GrowthBook’s sequential testing implementation (see this commit or the docs) but I have not contributed to them directly.