The paper introduces the R package **ummarked**, which provides a unified framework for fitting hierarchical models to ecological data collected using unmarked individual sampling protocols. These models are designed to address the challenges posed by imperfect detection and measurement errors in ecological research. The package supports various sampling methods, including site occupancy sampling, repeated counts, distance sampling, removal sampling, and double observer sampling. It offers tools for data exploration, model fitting, post-hoc analysis, and model comparison. The hierarchical structure of the models allows for separate modeling of the latent abundance or occurrence process and the conditional detection process, making it suitable for analyzing data from metapopulation designs. The package uses the S4 class system in R, allowing for flexible and customizable functions. Examples are provided to demonstrate how to prepare data, fit models, examine fitted models, perform model selection, and assess goodness of fit. Future developments aim to expand the range of sampling techniques covered, incorporate spatial dependence, optimize performance, and implement Bayesian inference.The paper introduces the R package **ummarked**, which provides a unified framework for fitting hierarchical models to ecological data collected using unmarked individual sampling protocols. These models are designed to address the challenges posed by imperfect detection and measurement errors in ecological research. The package supports various sampling methods, including site occupancy sampling, repeated counts, distance sampling, removal sampling, and double observer sampling. It offers tools for data exploration, model fitting, post-hoc analysis, and model comparison. The hierarchical structure of the models allows for separate modeling of the latent abundance or occurrence process and the conditional detection process, making it suitable for analyzing data from metapopulation designs. The package uses the S4 class system in R, allowing for flexible and customizable functions. Examples are provided to demonstrate how to prepare data, fit models, examine fitted models, perform model selection, and assess goodness of fit. Future developments aim to expand the range of sampling techniques covered, incorporate spatial dependence, optimize performance, and implement Bayesian inference.