  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.
