Time-series data are commonly collected as part of ecological studies, and increasingly ecologists want to analyze multivariate time-series data such as might arise when one has data on multiple sites or multiple environmental covariates. Variability enters such data through process error (stochasticity in the underlying ecological dynamics) and through observation error. In many ecological studies, the variability due to observation error cannot be independently estimated because it arises from changes in detectability due to some complex (often unknown) function of abiotic or biotic covariates. State-space modeling provides an established framework for analyzing time-series data with both process and observation error. Additionally, state-space models provide a framework for multiple observation time series, covariate time-series data, and missing observations and a framework for modeling parameters or variability with a hierarchical structure. This workshop will give participants an overview of state-space time-series modeling and give hands-on practice analyzing time-series data. The morning lectures will introduce autoregressive and state-space modeling. The afternoon will consist of three computer labs using R: estimation of PVA metrics from noisy and spotty multi-site count data, inference about environmental drivers and common trends, and analysis of the spatial structure within a population using multi-site data. The material will be presented at an introductory level, but participants need to have a basic understanding of likelihood inference (e.g. familiarity with the terms likelihood function, likelihood surface, and probability density function). The computer labs will be done in R, but knowledge of R is not necessary. Participants must provide laptops.