poems - Pattern-Oriented Ensemble Modeling System

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.

Last updated 4 months ago

biogeographypopulation-modelprocess-based

8.06 score 10 stars 2 dependents 61 scripts 196 downloads

colorednoise - Simulate Temporally Autocorrelated Populations

Temporally autocorrelated populations are correlated in their vital rates (growth, death, etc.) from year to year. It is very common for populations, whether they be bacteria, plants, or humans, to be temporally autocorrelated. This poses a challenge for stochastic population modeling, because a temporally correlated population will behave differently from an uncorrelated one. This package provides tools for simulating populations with white noise (no temporal autocorrelation), red noise (positive temporal autocorrelation), and blue noise (negative temporal autocorrelation). The algebraic formulation for autocorrelated noise comes from Ruokolainen et al. (2009) <doi:10.1016/j.tree.2009.04.009>. Models for unstructured populations and for structured populations (matrix models) are available.

Last updated 10 months ago

openblascpp

5.65 score 10 stars 18 scripts 340 downloads

epizootic - Spatially Explicit Population Models of Disease Transmission in Wildlife

This extension of the pattern-oriented modeling framework of the 'poems' package provides a collection of modules and functions customized for modeling disease transmission on a population scale in a spatiotemporally explicit manner. This includes seasonal time steps, dispersal functions that track disease state of dispersers, results objects that store disease states, and a population simulator that includes disease dynamics.

Last updated 4 months ago

cpp

5.41 score 3 stars 5 scripts 204 downloads

paleopop - Pattern-Oriented Modeling Framework for Coupled Niche-Population Paleo-Climatic Models

This extension of the poems pattern-oriented modeling (POM) framework provides a collection of modules and functions customized for paleontological time-scales, and optimized for single-generation transitions and large populations, across multiple generations.

Last updated 1 months ago

5.22 score 5 stars 11 scripts 309 downloads