The simmer package brings discrete-event simulation to R. It is designed as a generic yet powerful process-oriented framework. The architecture encloses a robust and fast simulation core written in C++ with automatic monitoring capabilities. It provides a rich and flexible R API that revolves around the concept of trajectory, a common path in the simulation model for entities of the same type.This manuscript corresponds to simmer version 3.6.4 and was typeset on December 05, 2017. For citations, please use the version accepted on the JSS when available. simmer: Discrete-Event Simulation for R systems, construction engineering, project management, logistics, transportation systems, business processes, healthcare and telecommunications networks (Banks 2005). The simulation of such systems provides insights into the process' risk, efficiency and effectiveness. Also, by simulation of an alternative configuration, one can proactively estimate the effects of changes to the system. In turn, this allows one to get clear insights into the benefits of process redesign strategies (e.g., extra resources). A wide range of practical applications is prompted by this, such as analysing bottlenecks in customer services centres, optimising patient flows in hospitals, testing the robustness of a supply chain or predicting the performance of a new protocol or configuration of a telecommunications network.There are several world views, or programming styles, for DES (Banks 2005). In the activityoriented approach, a model consists of sequences of activities, or operations, waiting to be executed depending on some conditions. The simulation clock advances in fixed time increments. At each step, the whole list of activities is scanned, and their conditions, verified. Despite its simplicity, the simulation performance is too sensitive to the election of such a time increment. Instead, the event-oriented approach completely bypasses this issue by maintaining a list of scheduled events ordered by time of occurrence. Then, the simulation just consists in jumping from event to event, sequentially executing the associated routines. Finally, the process-oriented approach refines the latter with the addition of interacting processes, whose activation is triggered by events. In this case, the modeller defines a set of processes, which correspond to entities or objects of the real system, and their life cycle. R> library(simmer) R> R> set.seed(1234) R> R> (env <-simmer("Job Shop")) simmer environment: Job Shop | now: 0 | next: R> RUNNING <-function() rexp(1, 1) R> RETOOL <-function() rexp(1, 2) R> RESET <-function() rexp(1, 3) R> AWAY <-function() rexp(1, 1) R> CHECK_WORN <-function() runif(1) < 0.2 R> NEW_JOB <-function() rexp(1, 5) R> NEW_TASK <-function() rexp(1, 1)The trajectory of an incoming job starts by seizing a machine in READY state. It takes some random time for RUNNING it after which the machine's serviceability is checked. An operative and some random time to RETOOL the machine may be needed, and either way an operative must RESET i...