Pattern-Oriented Ensemble Modeling System
R6 class representing a dispersal friction.
R6 class representing a dispersal generator.
R6 class representing a nested container for dispersal generator attri...
R6 class representing a nested container for generator attributes
R6 class representing a dynamic attribute generator
R6 class with generic reusable functionality
R6 class representing a generic manager.
R6 class representing a generic model.
R6 class to represent a Latin hypercube sampler.
R6 class representing a model simulator.
poems: Pattern-Oriented Ensemble Modeling System
poems: Pattern-oriented ensemble modeling and simulation
Nested functions for population density dependence.
Nested functions for population dispersal.
Nested functions for population environmental stochasticity.
Nested functions for initializing, calculating and collecting populati...
Runs a stage-based demographic population model simulation.
Nested functions for a user-defined population abundance (and capacity...
Nested functions for stage-based population transitions.
R6 class representing a population model
R6 class representing population simulator results.
R6 class representing a study region.
R6 class representing a results manager.
R6 class representing a simulation manager.
R6 class representing a simulation model
R6 class representing simulation results.
R6 class for a simulator reference
R6 class representing a spatial correlation.
R6 class representing a spatial model
R6 class representing a pattern-oriented validator.
A framework of interoperable R6 classes (Chang, 2020, <https://CRAN.R-project.org/package=R6>) for building ensembles of viable models via the pattern-oriented modeling (POM) approach (Grimm et al.,2005, <doi:10.1126/science.1116681>). The package includes classes for encapsulating and generating model parameters, and managing the POM workflow. The workflow includes: model setup; generating model parameters via Latin hyper-cube sampling (Iman & Conover, 1980, <doi:10.1080/03610928008827996>); running multiple sampled model simulations; collating summary results; and validating and selecting an ensemble of models that best match known patterns. By default, model validation and selection utilizes an approximate Bayesian computation (ABC) approach (Beaumont et al., 2002, <doi:10.1093/genetics/162.4.2025>), although alternative user-defined functionality could be employed. The package includes a spatially explicit demographic population model simulation engine, which incorporates default functionality for density dependence, correlated environmental stochasticity, stage-based transitions, and distance-based dispersal. The user may customize the simulator by defining functionality for translocations, harvesting, mortality, and other processes, as well as defining the sequence order for the simulator processes. The framework could also be adapted for use with other model simulators by utilizing its extendable (inheritable) base classes.
Useful links