The increasing availability of complex survey data, and the continued need for estimates of demographic and health indicators at a fine spatial and temporal scale, which leads to issues of data sparsity, has led to the need for spatio-temporal smoothing methods that acknowledge the manner in which the data were collected. The open source R package SUMMER implements a variety of methods for spatial or spatio-temporal smoothing of survey data. The emphasis is on small-area estimation. We focus primarily on indicators in a low and middle-income countries context. Our methods are particularly useful for data from Demographic Health Surveys and Multiple Indicator Cluster Surveys. We build upon functions within the survey package, and use INLA for fast Bayesian computation. This paper includes a brief overview of these methods and illustrates the workflow of accessing and processing surveys, estimating subnational child mortality rates, and visualizing results with both simulated data and DHS surveys.