The R Journal · 2014 · 64 citations · 10 references
EngineeringData ScienceStatistical ModelingStochastic ModelingStochastic ProcessesBrownian MotionDensity EstimationDiffusion ProcessesDiffusion ModelsDiffusionsInference For Diffusion ProcessesLikelihood-based InferenceLog-likelihood FunctionsParameter Estimation For DiffusionsParameter Estimation For Stochastic Differential EquationsWiener Models
We present the RWiener package that provides R functions for the Wiener diffusion model. The core of the package are the four distribution functions dwiener, pwiener, qwiener and rwiener, which use up-to-date methods, implemented in C, and provide fast and accurate computation of the density, distribution, and quantile function, as well as a random number generator for the Wiener diffusion model. We used the typical Wiener diffusion model with four parameters: boundary separation, non-decision time, initial bias and drift rate parameter. Beyond the distribution functions, we provide extended likelihood-based functions that can be used for parameter estimation and model selection. The package can be obtained via CRAN.
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AIC model selection using Akaike weights
Eric‐Jan Wagenmakers, Simon Farrell · Psychonomic Bulletin & Review · 2004 · 2.8K citations · Full text