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://biorabbit.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:

3.18 score 3 stars 4 scripts 237 downloads 3 exports 62 dependencies

Last updated from:150906914f. Checks:7 ERROR, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel	ERROR	155
source / vignettes	OK	250
linux-release	ERROR	165
macos-release	ERROR	154
macos-oldrel	ERROR	138
windows-devel	ERROR	131
windows-release	ERROR	117
windows-oldrel	ERROR	96
wasm-release	OK	136

Exports:generate_sim_data get_stan_fit regddm

Dependencies:abind backports BH callr checkmate cli cpp11 desc distributional dplyr evd expm farver generics ggplot2 glue gridExtra gsl gtable inline isoband labeling lattice lifecycle loo magrittr Matrix matrixStats msm mvtnorm numDeriv otel pillar pkgbuild pkgconfig posterior processx ps purrr QuickJSR R6 RColorBrewer Rcpp RcppEigen RcppParallel rlang rstan rtdists S7 scales StanHeaders stringi stringr survival tensorA tibble tidyr tidyselect utf8 vctrs viridisLite withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Generate simulated binary decision data using DDM	generate_sim_data
Get the rstan fit of regddmfit objects	get_stan_fit
Printed summary of 'RegDDM' fit object	print.regddmfit
Bayesian hierarchical generalized linear regression using Drift-Diffusion Model	regddm
Simulated dataset from from Cognitive Reserve Study.	regddm_data
Class of models fitted by 'RegDDM'	regddmfit
Summary of 'RegDDM' fit object	summary.regddmfit