Package: RegDDM 1.1

Zekai Jin

RegDDM: Generalized Linear Regression with DDM

Drift-Diffusion Model (DDM) has been widely used to model binary decision-making tasks, and many research studies the relationship between DDM parameters and other characteristics of the subject. This package uses 'RStan' to perform generalized liner regression analysis over DDM parameters via a single Bayesian Hierarchical model. Compared to estimating DDM parameters followed by a separate regression model, 'RegDDM' reduces bias and improves statistical power.

Authors:Zekai Jin [aut, cre]

RegDDM_1.1.tar.gz
RegDDM_1.1.zip(r-4.7-any)RegDDM_1.1.zip(r-4.6-any)RegDDM_1.1.zip(r-4.5-any)
RegDDM_1.1.tgz(r-4.6-any)RegDDM_1.1.tgz(r-4.5-any)
RegDDM_1.1.tar.gz(r-4.7-any)RegDDM_1.1.tar.gz(r-4.6-any)
RegDDM_1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
RegDDM/json (API)

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

Bug tracker:https://github.com/biorabbit/regddm/issues

Datasets:
  • regddm_data - Simulated dataset from from Cognitive Reserve Study.

On CRAN:

Conda:

archivedpackagesr-universe

2.40 score 5 stars 4 scripts 235 downloads 3 exports 62 dependencies

Last updated from:f45662d357 (on package/RegDDM). Checks:7 ERROR, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develERROR149
source / vignettesOK236
linux-releaseERROR153
macos-releaseERROR110
macos-oldrelERROR101
windows-develERROR104
windows-releaseERROR103
windows-oldrelERROR109
wasm-releaseOK132

Exports:generate_sim_dataget_stan_fitregddm

Dependencies:abindbackportsBHcallrcheckmateclicpp11descdistributionaldplyrevdexpmfarvergenericsggplot2gluegridExtragslgtableinlineisobandlabelinglatticelifecycleloomagrittrMatrixmatrixStatsmsmmvtnormnumDerivotelpillarpkgbuildpkgconfigposteriorprocessxpspurrrQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelrlangrstanrtdistsS7scalesStanHeadersstringistringrsurvivaltensorAtibbletidyrtidyselectutf8vctrsviridisLitewithr