Package: fddm 1.0-2

Henrik Singmann

fddm: Fast Implementation of the Diffusion Decision Model

Provides the probability density function (PDF), cumulative distribution function (CDF), the first-order and second-order partial derivatives of the PDF, and a fitting function for the diffusion decision model (DDM; e.g., Ratcliff & McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) with across-trial variability in the drift rate. Because the PDF, its partial derivatives, and the CDF of the DDM both contain an infinite sum, they need to be approximated. 'fddm' implements all published approximations (Navarro & Fuss, 2009, <doi:10.1016/j.jmp.2009.02.003>; Gondan, Blurton, & Kesselmeier, 2014, <doi:10.1016/j.jmp.2014.05.002>; Blurton, Kesselmeier, & Gondan, 2017, <doi:10.1016/j.jmp.2016.11.003>; Hartmann & Klauer, 2021, <doi:10.1016/j.jmp.2021.102550>) plus new approximations. All approximations are implemented purely in 'C++' providing faster speed than existing packages.

Authors:Kendal B. Foster [aut], Henrik Singmann [ctb, cre], Achim Zeileis [ctb]

fddm_1.0-2.tar.gz
fddm_1.0-2.zip(r-4.7)fddm_1.0-2.zip(r-4.6)fddm_1.0-2.zip(r-4.5)
fddm_1.0-2.tgz(r-4.6-x86_64)fddm_1.0-2.tgz(r-4.6-arm64)fddm_1.0-2.tgz(r-4.5-x86_64)fddm_1.0-2.tgz(r-4.5-arm64)
fddm_1.0-2.tar.gz(r-4.7-arm64)fddm_1.0-2.tar.gz(r-4.7-x86_64)fddm_1.0-2.tar.gz(r-4.6-arm64)fddm_1.0-2.tar.gz(r-4.6-x86_64)
fddm_1.0-2.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
fddm/json (API)

# Install 'fddm' in R:
install.packages('fddm', repos = c('https://rtdists.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/rtdists/fddm/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

cpp

5.65 score 18 stars 4 scripts 452 downloads 15 exports 3 dependencies

Last updated from:7780857f2e. Checks:11 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64ERROR206
linux-devel-x86_64ERROR226
source / vignettesOK296
linux-release-arm64ERROR207
linux-release-x86_64ERROR199
macos-release-arm64ERROR125
macos-release-x86_64ERROR353
macos-oldrel-arm64ERROR109
macos-oldrel-x86_64ERROR435
windows-develERROR211
windows-releaseERROR168
windows-oldrelERROR200
wasm-releaseOK216

Exports:da_dfddmda2_dfddmddmdfddmdsv_dfddmdsv2_dfddmdt_dfddmdt0_dfddmdt02_dfddmdt2_dfddmdv_dfddmdv2_dfddmdw_dfddmdw2_dfddmpfddm

Dependencies:FormulaRcppRcppEigen

Benchmark Testing for the DDM Density Function
Introduction | Benchmarking the Density Function Approximations | Generating Benchmark Data | Analysis of Benchmark Results | Benchmarking Model Fitting to Real-World Data | Generating Benchmark Data for Parameter Estimation | Log-Likelihood Functions | Fitting Function | Running the Fitting | Analysis of Benchmark Results | References

Last update: 2024-07-04
Started: 2020-06-01

Description of Methods in pfddm
Mathematical Background | The Distribution Function Approximations | Infinite Sum Methods | Normal CDF | Mills Ratio | PDE Method | Benchmarking the Distribution Function Approximations | Vectorized Benchmark Data and Results | Non-vectorized Benchmark Data and Results | References

Last update: 2024-06-25
Started: 2021-07-27

Fitting Examples Using fddm
Introduction | Fitting with ddm() | Simple Fitting Routine | Rudimentary Analysis | Fitting Manually with dfddm() | Log-likelihood Function | Simple Fitting Routine | Fitting the Entire Dataset | Rudimentary Analysis | References

Last update: 2024-06-25
Started: 2020-06-01

Mathematical Description of Methods in dfddm
Mathematical Background | The Density Function Approximations | Large-Time | Small-Time | Navarro & Fuss | Gondan, Blurton, Kesselmeier | Stop When Small Enough (SWSE) | Combining Large-Time and Small-Time | Navarro Small & Navarro Large | Gondan Small & Navarro Large | Stop When Small Enough (SWSE) Small & Navarro Large | First Heuristic: Large-Time Efficiency | Second Heuristic: Effective Response Time | References

Last update: 2024-06-25
Started: 2020-05-04

Validity of Methods in dfddm
Background | Validating the Density Function Approximations | Generating Data | Testing the Density Function Approximations | Known Errors (KE) | Validating Fitting (Optimization) Using the Density Function Approximations | Generating the Parameter Estimates Using Real-World Data | Log-Likelihood Functions | Fitting Function | Running the Fitting | Testing the Fitted Parameters | References

Last update: 2024-06-25
Started: 2020-05-04

Readme and manuals

Help Manual

Help pageTopics
Partial Derivative of 5-parameter DDM PDF with respect to a (threshold separation)da_dfddm
Second-Order Partial Derivative of 5-parameter DDM PDF with respect to a (threshold separation)da2_dfddm
Estimation of 5-Parameter DDMddm
Methods for ddm objectscoef.ddm ddm-methods emm_basis.ddm logLik.ddm model.frame.ddm model.matrix.ddm print.ddm print.summary.ddm recover_data.ddm summary.ddm terms.ddm update.ddm vcov.ddm
Density of Ratcliff Diffusion Decision Modeldfddm
Partial Derivative of 5-parameter DDM PDF with respect to sv (inter-trial variability in the drift rate)dsv_dfddm
Second-Order Partial Derivative of 5-parameter DDM PDF with respect to sv (inter-trial variability in the drift rate)dsv2_dfddm
Partial Derivative of 5-parameter DDM PDF with respect to t (response time)dt_dfddm
Partial Derivative of 5-parameter DDM PDF with respect to t0 (non-decision time)dt0_dfddm
Second-Order Partial Derivative of 5-parameter DDM PDF with respect to t0 (non-decision time)dt02_dfddm
Second-Order Partial Derivative of 5-parameter DDM PDF with respect to t (response time)dt2_dfddm
Partial Derivative of 5-parameter DDM PDF with respect to v (drift rate)dv_dfddm
Second-Order Partial Derivative of 5-parameter DDM PDF with respect to v (drift rate)dv2_dfddm
Partial Derivative of 5-parameter DDM PDF with respect to w (initial bias)dw_dfddm
Second-Order Partial Derivative of 5-parameter DDM PDF with respect to w (initial bias)dw2_dfddm
Medicial decision datamed_dec
Distribution of Ratcliff Diffusion Decision Modelpfddm