dRiftDM: Estimating (Time-Dependent) Drift Diffusion Models
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: |
0.2.1 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
withr, parallel, DEoptim, dfoptim, Rcpp, Rdpack, progress, stats |
| LinkingTo: |
Rcpp |
| Suggests: |
testthat (≥ 3.0.0), cowsay, knitr, rmarkdown, DMCfun, truncnorm, vdiffr |
| Published: |
2025-01-08 |
| DOI: |
10.32614/CRAN.package.dRiftDM |
| Author: |
Valentin Koob [cre, aut, cph],
Thomas Richter [aut, cph],
Markus Janczyk [ctb] |
| Maintainer: |
Valentin Koob <v.koob at web.de> |
| BugReports: |
https://github.com/bucky2177/dRiftDM/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/bucky2177/dRiftDM,
https://bucky2177.github.io/dRiftDM/ |
| NeedsCompilation: |
yes |
| Citation: |
dRiftDM citation info |
| Materials: |
README NEWS |
| CRAN checks: |
dRiftDM results |
Documentation:
Downloads:
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