Post-treatment confounding is a common problem in causal inference, with special cases of noncompliance, truncation by death, surrogate endpoint. Principal stratification is a general framework for defining and estimating causal effects in the presence of post-treatment confounding. Despite its versatility, principal stratification is not accessible to the vast majority of applied researchers because its inherent latent structure, which requires complex inference tools and highly-customized programming. We fill this gap by developing a computing platform R package "PStrata" to automatize inference of principal stratification for the most common scenarios. PStrata supports two inference paradigms and accommodate different assumptions. For the Bayesian paradigm, the computing architecture combines R, C ++, Stan, where R provides the user-interface, Stan automatizes posterior sampling, and C++ bridges the two owing. For the frequentist paradigm, PStrata implements the weighting-based triply-robust estimator of Jiang et al (2020). PStrata can accommodate regular outcomes and time-to-event outcomes. We will illustrate PStrata using a high-profile clinical trial on aspirin use.