newRandomMixing

A high-performance, flexible and extensible framework to
develop continuous-time agent based models. Its high performance
allows it to simulate millions of agents efficiently. Agents are
defined by their states (arbitrary R lists). The events are handled in
chronological order. This avoids the multi-event interaction problem
in a time step of discrete-time simulations, and gives precise
outcomes. The states are modified by provided or user-defined events.
The framework provides a flexible and customizable implementation of
state transitions (either spontaneous or caused by agent
interactions), making the framework suitable to apply to epidemiology
and ecology, e.g., to model life history stages, competition and
cooperation, and disease and information spread. The agent
interactions are flexible and extensible. The framework provides
random mixing and network interactions, and supports multi-level
mixing patterns.  It can be easily extended to other interactions such
as inter- and intra-households (or workplaces and schools) by
subclassing an R6 class. It can be used to study the effect of
age-specific, group-specific, and contact- specific intervention
strategies, and complex interactions between individual behavior and
population dynamics. This modeling concept can also be used in
business, economical and political models. As a generic event based
framework, it can be applied to many other fields. More information
about the implementation and examples can be found at
<https://github.com/junlingm/ABM>.

newRandomMixing: Creates a RandomMixing object

Description

Arguments

Value

Examples