Evolutionary Parameter Estimation for 'Repast Simphony' Models

The EvoPER, Evolutionary Parameter Estimation for 'Repast Simphony' Agent-Based framework (< https://repast.github.io/>), provides optimization driven parameter estimation methods based on evolutionary computation techniques which could be more efficient and require, in some cases, fewer model evaluations than other alternatives relying on experimental design.


Change Log

This project adheres to Semantic Versioning.

  • Estimates class for returning results of extremize function
  • OptionsACOR class for ACOr Options
  • abm.acor Implementation of ACO for continuous domains
  • abm.ees1 EvoPER Implementation of simple ES
  • OptionsFactory Creates an instance of Options class
  • naiveperiod Search for period in data
  • gm.mean Calculate the geometric mean
  • elog.level Wrapper (futile.logger) for getting/setting log level
  • elog.error Wrapper (futile.logger) for logging error messages
  • elog.info Wrapper (futile.logger) for logging info messages
  • elog.debug Wrapper (futile.logger) for logging debug messages
  • acor.updateants ACO internal function
  • acor.weigth ACO internal function
  • acor.probabilities ACO internal function
  • acor.lthgaussian ACO internal function
  • acor.sigma ACO internal function
  • acor.archive ACO internal function
  • acor.S ACO internal function
  • acor.F ACO internal function
  • acor.W ACO internal function
  • acor.N ACO internal function
  • es.evaluate General ES function for evaluating a solution
  • ees1.mating EES1 internal function
  • ees1.mating1 EES1 internal function
  • ees1.recombination EES1 internal function
  • ees1.mutation EES1 internal function
  • ees1.challenge EES1 internal function
  • ees1.explore EES1 internal function
  • ees1.selection EES1 internal function
  • abm.ees2 EvoPER Implementation of simple ES
  • partSolutionSpace Support function for implementing evolutionary strategies
  • sortSolution Support function for implementing evolutionary strategies
  • bestFitness Support function for implementing evolutionary strategies
  • getFitness Support function for implementing evolutionary strategies
  • bestSolution Support function for implementing evolutionary strategies
  • getSolution Support function for implementing evolutionary strategies
  • slopes Calculate the slope between poins for a time series
  • slope Calculate the slope between poins for a time series
  • gm.mean Calculate geometric mean
  • naiveperiod Calculate the period for a time series
  • Magnitude Returns the magnitude for a given number
  • xmeanci1 Calculates the confidence interval using resampling
  • xmeanci2 Calculates the confidence interval
  • fixdfcolumns Convert data frame columns to a type
  • xyplothelper Generate a xy plot
  • histplothelper Generate a histogram plot
  • scatterplotlothelper Generate a scatter plot
  • contourplothelper Generate a surface plot
  • f0.adtn.rosenbrock2 Examples, rosenbrock with noise
  • f1.adtn.rosenbrock2 Examples, rosenbrock with noise
  • f0.nlnn.rosenbrock2 Examples, rosenbrock with noise
  • f1.nlnn.rosenbrock2 Examples, rosenbrock with noise
  • predatorprey Examples, predator-prey ODE
  • predatorprey.plot Examples, plot results of predator-prey
  • f0.periodtuningpp Examples, period tuning of predator-prey
  • extremize now returns an instance of Estimates class
  • initSolution now accepts multiple sampling schemes
  • extremize the entry point for optimization functions
  • Options Parent class for options
  • OptionsPSO class for PSO Options
  • OptionsSAA class for SAA Options
  • OptionsSDA class for SDA Options
  • abm.sda Simulted dilution
  • saa.neighborhood Neighborhood for SAA
  • saa.neighborhood1 Neighborhood for SAA
  • saa.neighborhoodH Neighborhood for SAA
  • saa.neighborhoodN Neighborhood for SAA
  • saa.tbyk Temperature for SAA
  • saa.texp Temperature for SAA
  • saa.bolt Temperature for SAA
  • saa.tcte Temperature for SAA
  • f0.test Plain Test function
  • f0.rosenbrock2 Plain Rosenbrock 2 parms test function
  • abm.pso Change in parameters
  • abm.saa Change in parameters
  • abm.sda Change in parameters
  • renamed saa.neighborhood.t1 now is - saa.neighborhood1
  • Bug in abm.pso function
  • ObjectiveFunction S4 Parent class for the objective function
  • PlainFunction S4 Child class of ObjectiveFunction for estimating parameters of plain math functions
  • RepastFunction S4 Child class of ObjectiveFunction for estimating parameters of Repast models
  • assert Checks if some required precondition holds and stop execution if not
  • pso.Velocity Calculate the particle velocity
  • pso.neighborhood.K2 Two neighbors
  • pso.neighborhood.K4 Non Neumann Neighborhood for Particle Searm
  • pso.neighborhood.KN Fully connected graph neighborhood
  • pso.lbest Seach for the best neighbor
  • pso.best Search for the best particle
  • pso.chi Calculates the constriction coefficient
  • cbuf A Simple circular buffer implementation
  • lowerBound Check lower bound
  • upperBound Check upper bound
  • enforceBounds Check the lower and upper bounds
  • saa.neighborhood.t1 Neighborhood function for Simulated Annealing
  • abm.pso Entry point for Particle Swarm Optimization
  • abm.saa Entry point for Simulated Annealing
  • initSolution Generates a initial solution or particles

Reference manual

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0.4.0 by Antonio Prestes Garcia, 8 months ago


Report a bug at https://github.com/antonio-pgarcia/evoper/issues

Browse source code at https://github.com/cran/evoper

Authors: Antonio Prestes Garcia [aut, cre], Alfonso Rodriguez-Paton [aut, ths]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports methods, futile.logger, boot, reshape, ggplot2, deSolve, plot3D, plyr

Depends on rrepast

Suggests testthat

See at CRAN