dRiftDM: Estimating (Time-Dependent) Drift Diffusion Models (original) (raw)
Fit and explore Drift Diffusion Models (DDMs), a common tool in psychology for describing decision processes in simple tasks. It can handle both time-independent and time-dependent DDMs. You either choose prebuilt models or create your own, and the package takes care of model predictions and parameter estimation. Model predictions are derived via the numerical solutions provided by Richter, Ulrich, and Janczyk (2023, <doi:10.1016/j.jmp.2023.102756>).
| Version: | 0.2.2 |
|---|---|
| Depends: | R (≥ 4.1.0) |
| Imports: | withr, parallel, DEoptim, dfoptim, Rcpp, Rdpack, progress, stats, lifecycle |
| LinkingTo: | Rcpp |
| Suggests: | testthat (≥ 3.0.0), cowsay, knitr, rmarkdown, DMCfun, truncnorm, vdiffr |
| Published: | 2025-03-04 |
| DOI: | 10.32614/CRAN.package.dRiftDM |
| Author: | Valentin Koob [cre, aut, cph], Thomas Richter [aut, cph], Markus Janczyk [aut] |
| Maintainer: | Valentin Koob <v.koob at web.de> |
| BugReports: | https://github.com/bucky2177/dRiftDM/issues |
| License: | MIT + file |
| URL: | https://github.com/bucky2177/dRiftDM,https://bucky2177.github.io/dRiftDM/ |
| NeedsCompilation: | yes |
| Citation: | dRiftDM citation info |
| Materials: | README, NEWS |
| CRAN checks: | dRiftDM results |
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