  Verbal autopsy (VA) is a survey-based tool widely used to infer cause of
death (COD) in regions without complete-coverage civil registration and vital
statistics systems. In such settings, many deaths happen outside of medical
facilities and are not officially documented by a medical professional. VA
surveys, consisting of signs and symptoms reported by a person close to the
decedent, are used to infer the cause of death for an individual, and to
estimate and monitor the cause of death distribution in the population. Several
classification algorithms have been developed and widely used to assign cause
of death using VA data. However, The incompatibility between different
idiosyncratic model implementations and required data structure makes it
difficult to systematically apply and compare different methods. The openVA
package provides the first standardized framework for analyzing VA data that is
compatible with all openly available methods and data structure. It provides an
open-sourced, R implementation of several most widely used VA methods. It
supports different data input and output formats, and customizable information
about the associations between causes and symptoms. The paper discusses the
relevant algorithms, their implementations in R packages under the openVA
suite, and demonstrates the pipeline of model fitting, summary, comparison, and
visualization in the R environment.
