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Abstract

This article describes the R package DEoptim which implements the differential evolution algorithm for the global optimization of a real-valued function of a real-valued parameter vector. The implementation of differential evolution in DEoptim interfaces with C code for efficiency. The utility of the package is illustrated via case studies in fitting a Parratt model for X-ray reflectometry data and a Markov-Switching GeneralizedAutoRegressive Conditional Heteroskedasticity MSGARCH model for the returns of the Swiss Market Index.



Item Type: MPRA Paper -

Original Title: DEoptim: An R Package for Global Optimization by Differential Evolution-

Language: English-

Keywords: global optimization; evolutionary algorithm; differential evolution; R software-

Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and EstimationC - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic AnalysisC - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion ProcessesC - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: GeneralC - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C80 - General-





Author: Mullen, Katharine M.

Source: https://mpra.ub.uni-muenchen.de/21743/



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