Post-Linkage Data Analysis // Provides a suite of statistical tools for post-linkage data analysis (PLDA), designed to account for record linkage errors in downstream modeling. The package implements a familiar, formula-based regression interface that adjusts for linkage uncertainty, accommodating workflows where direct access to unlinked primary files is restricted. It consolidates diverse adjustment methodologies, all of which support generalized linear models (linear, logistic, Poisson, and Gamma). These methodologies include weighting approaches (Chambers (2009) https://hdl.handle.net/10779/uow.27788247; Chambers et al. (2023) doi:10.1002/wics.1596), mixture modeling (Slawski et al. (2025) doi:10.1093/jrsssa/qnae083), and Bayesian mixture modeling (Gutman et al. (2016) doi:10.1002/sim.6586). For time- to-event data, both the weighting (Vo et al. (2024) doi:10.1002/sim.9960) and mixture modeling approaches accommodate Cox proportional hazards models, while the Bayesian approaches extend to parametric survival analysis. Additionally, the package leverages mixture modeling for contingency table analyses and Bayesian methods to enable the multiple imputation of latent match status.