Package: glba 0.2.1

glba: General Linear Ballistic Accumulator Models

Analyses response times and accuracies from psychological experiments with the linear ballistic accumulator (LBA) model from Brown and Heathcote (2008). The LBA model is optionally fitted with explanatory variables on the parameters such as the drift rate, the boundary and the starting point parameters. A log-link function on the linear predictors can be used to ensure that parameters remain positive when needed.

Authors:Ingmar Visser

glba_0.2.1.tar.gz
glba_0.2.1.zip(r-4.5)glba_0.2.1.zip(r-4.4)glba_0.2.1.zip(r-4.3)
glba_0.2.1.tgz(r-4.4-any)glba_0.2.1.tgz(r-4.3-any)
glba_0.2.1.tar.gz(r-4.5-noble)glba_0.2.1.tar.gz(r-4.4-noble)
glba_0.2.1.tgz(r-4.4-emscripten)glba_0.2.1.tgz(r-4.3-emscripten)
glba.pdf |glba.html
glba/json (API)

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

Peer review:

Datasets:
  • bh08 - Example data from Brown and Heathcote (2008).
  • ilpp2 - Implicit learning data from Visser et al (2007).
  • numpp1 - Example data from a numerosity task.

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3 exports 1 stars 0.09 score 0 dependencies 6 scripts 570 downloads

Last updated 2 years agofrom:2e43a7bd8c. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 25 2024
R-4.5-winOKAug 25 2024
R-4.5-linuxOKAug 25 2024
R-4.4-winOKAug 25 2024
R-4.4-macOKAug 25 2024
R-4.3-winOKAug 25 2024
R-4.3-macOKAug 25 2024

Exports:lbarlbatablba

Dependencies: