Regime types and transitions are central to a wide range of political phenomena. Reflecting this importance, prior research has produced a variety of regime measures. This diversity, however, comes with important challenges for applied research: selecting a measure among many options, having to define regime categories based on cut-offs, identifying regime transitions by specific magnitudes of change over a specific time window and dealing with measurement uncertainty and missing data. In this article, we introduce Unified Transitions and Stability (UNITAS), a new framework that offers a solution to these challenges. Combining information from commonly used regime indicators, this approach identifies regime types and transitions probabilistically, locates the most likely periods of regime transitions and incorporates measurement uncertainty. Through Monte Carlo simulations, we demonstrate the desirable properties and robustness of UNITAS under various scenarios. In an illustrative application, we show that stable semi-democracies are not inherently conflict-prone and that autocratization is consistently associated with higher civil war risk while democratization is not.