Addresses tasks along the pipeline from raw
data to analysis and visualization for eye-tracking data. Offers several
popular types of analyses, including linear and growth curve time analyses,
onset-contingent reaction time analyses, as well as several non-parametric
bootstrapping approaches. For references to the approach see Mirman,
Dixon & Magnuson (2008) <doi:10.1016/j.jml.2007.11.006>, and
Barr (2008) <doi:10.1016/j.jml.2007.09.002>.
| Version: |
0.2.1 |
| Depends: |
R (≥ 3.2.0), dplyr (≥ 0.7.4) |
| Imports: |
broom (≥ 0.3.7), broom.mixed, ggplot2 (≥ 2.0), lazyeval (≥
0.1.10), rlang, zoo (≥ 1.7-12), tidyr (≥ 0.3.1), purrr (≥
0.2.4) |
| Suggests: |
pbapply, knitr, lme4 (≥ 1.1-10), glmmTMB, MASS, Matrix, testthat, rmarkdown, doMC, foreach |
| Published: |
2023-09-15 |
| DOI: |
10.32614/CRAN.package.eyetrackingR |
| Author: |
Samuel Forbes [aut, cre],
Jacob Dink [aut],
Brock Ferguson [aut] |
| Maintainer: |
Samuel Forbes <samuel.h.forbes at gmail.com> |
| BugReports: |
https://github.com/samhforbes/eyetrackingR/issues |
| License: |
MIT + file LICENSE |
| URL: |
http://samforbes.me/eyetrackingR/ |
| NeedsCompilation: |
no |
| Citation: |
eyetrackingR citation info |
| Materials: |
README NEWS |
| CRAN checks: |
eyetrackingR results |