Package: rcicr 1.5.0.9000

rcicr: Reverse-Correlation Image-Classification Toolbox

Generate stimuli and analyze data of reverse correlation image classification experiments (psychophysical tasks aimed at visualizing cognitive mental representations of faces). For the method see Dotsch and Todorov (2012) <doi:10.1177/1948550611430272>; for a practical primer see Brinkman, Todorov and Dotsch (2017) <doi:10.1080/10463283.2017.1381469>.

Authors:Ron Dotsch [aut, cre]

rcicr_1.5.0.9000.tar.gz
rcicr_1.5.0.9000.zip(r-4.7-any)rcicr_1.5.0.9000.zip(r-4.6-any)rcicr_1.5.0.9000.zip(r-4.5-any)
rcicr_1.5.0.9000.tgz(r-4.6-any)rcicr_1.5.0.9000.tgz(r-4.5-any)
rcicr_1.5.0.9000.tar.gz(r-4.7-any)rcicr_1.5.0.9000.tar.gz(r-4.6-any)
rcicr_1.5.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
rcicr/json (API)

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

Bug tracker:https://github.com/rdotsch/rcicr/issues

Pkgdown/docs site:https://rdotsch.github.io

On CRAN:

Conda:

7.75 score 9 stars 47 scripts 462 downloads 19 exports 41 dependencies

Last updated from:4445b3d257. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK166
source / vignettesOK225
linux-releaseOK187
macos-releaseOK186
macos-oldrelOK132
windows-develOK174
windows-releaseOK167
windows-oldrelOK160
wasm-releaseOK110

Exports:autoscalebatchComputeInfoVal2IFCbatchGenerateCIbatchGenerateCI2IFCcheckStimulusPNGs2IFCcomputeCumulativeCICorrelationcomputeInfoVal2IFCdeg2radgenerateCIgenerateCI2IFCgenerateCINoisegenerateGaborgenerateNoiseImagegenerateNoisePatterngenerateReferenceDistribution2IFCgenerateSinusoidgenerateStimuli2IFCplotZmapsimulateNoiseIntensities

Dependencies:abindclicodetoolscpp11deldirdoSNOWfarverforeachggplot2gluegoftestgridExtragtableisobanditeratorsjpeglabelinglatticelifecycleMatrixnlmepngpolyclipR6RColorBrewerrlangS7scalessnowspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilstensorvctrsviridisviridisLitewithr

Recipes
A stimulus set and a simulated participant | Noise-only stimuli | Ratings instead of a two-image choice | Matching the InfoVal reference to the design | When some trials were dropped | A masked classification image | Several classification images at once | A CI averaged over participants | Reproducing a number from an earlier version | Run the version that computed it | Recompute with the current version | When the .Rdata file is lost | Checking the settings against the stimulus PNGs
Rendered fromrecipes.Rmdusingknitr::rmarkdown

Last update: 2026-10-04
Started: 2026-10-03

A reverse correlation walkthrough
1. Installing | 2. What the method does | 3. Generating stimuli | The .Rdata file is the important output | 4. Collecting responses | 5. Computing one classification image | 6. Scaling | 7. Several participants at once | After autoscale(), look at $scaled | 8. Is there actually signal? | 9. Running the task online | 10. Citing
Rendered fromreverse-correlation-walkthrough.Rmdusingknitr::rmarkdown

Last update: 2026-10-04
Started: 2026-07-27

What rcicr stores
The stimulus .Rdata file | Written when the stimuli are generated | Added when an informational value is computed | Attributes | Before you write code against the file | The classification image
Rendered fromstored-data.Rmdusingknitr::rmarkdown

Last update: 2026-10-04
Started: 2026-10-03

Getting started with rcicr
1. Generate stimuli | 2. Collect (or, here, simulate) responses | 3. Compute the classification image | Next steps
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-09-24
Started: 2026-07-26