The package dRiftDM was developed to assist psychology researchers in applying and fitting diffusion models to empirical data within the R environment. Its most important feature is the ability to handle non-stationary problems, specifically diffusion models with time-dependent parameters. The package includes essential tools for standard analyses, such as building models, estimating parameters for multiple participants (individually for each participant), and creating summary statistics. The pre-built models available in the package are:
Users can flexibly create custom models and utilize the dRiftDM machinery for estimating them.
Starting with version 0.2.0, model predictions (i.e., first-passage times) are derived by numerically solving the Kolmogorov-Forward Equation or a coupled set of integral equations, based on code provided by Richter et al. (2023, Journal of Mathematical Psychology).
NotesCompared to the previous version 0.1.1, versions >0.2.0 make greater use of the S3 object system. Additionally, beginning with version 0.2.0, models use âflex_prmsâ objects to handle parameters across conditions.
To install the older version (0.1.1), you can use:
devtools::install_github("bucky2177/dRiftDM", ref = "0.1.1")You can install the development version of dRiftDM from GitHub with:
# install.packages("devtools")
devtools::install_github("bucky2177/dRiftDM")The CRAN version can be installed with:
install.packages("dRiftDM")If you are interested in getting started with dRiftDM, we recommend reading the OSF pre-print. More information on functions and model customization can be found in dRiftDMâs vignettes. These vignettes are also available from the âGetting startedâ and âArticlesâ tabs on our Github.io page.
If you have any questions, feel free to contact us!
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