Package: grt 0.2.1

grt: General Recognition Theory

Functions to generate and analyze data for psychology experiments based on the General Recognition Theory.

Authors:Kazunaga Matsuki

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grt.pdf |grt.html✨
grt/json (API)

# Install 'grt' in R:
install.packages('grt', repos = c('https://ajwills72.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • subjdemo_1d - Sample dataset of a categorization experiment with 1D stimuli.
  • subjdemo_2d - Sample dataset of a categorization experiment with 2D stimuli.
  • subjdemo_3d - Sample dataset of a categorization experiment with 3D stimuli.
  • subjdemo_cj - Sample dataset of a categorization experiment with 2D conjunctive stimuli.

On CRAN:

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

50 exports 0.00 score 1 dependencies 44 scripts 233 downloads

Last updated 7 years agofrom:7a682f6c30. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 18 2024
R-4.5-winNOTESep 18 2024
R-4.5-linuxNOTESep 18 2024
R-4.4-winNOTESep 18 2024
R-4.4-macNOTESep 18 2024
R-4.3-winOKSep 18 2024
R-4.3-macOKSep 18 2024

Exports:angle2cartcart2anglecoef.gcjccoef.glccoef.glcStructdprimedprimefextractAIC.gcjcextractAIC.glcextractAIC.gqcextractAIC.grggaborPatchgcjcgcjcStructglcglcStructgqcgqcStructgrggrtMeansgrtrnormldbldb.p.correctlines.gqcStructlogLik.gcjclogLik.gcjcStructlogLik.glclogLik.glcStructlogLik.gqclogLik.gqcStructmcovsmcovs.defaultmcovs.formulanew2old_parold2new_parplot.gcjcplot.glcplot.gqcplot3d.glcplot3d.gqcpredict.glcprint.gcjcprint.glcprint.gqcqdbqdb.p.correctscalescale.glcscale.gqcunscale

Dependencies:MASS

Readme and manuals

Help Manual

Help pageTopics
General Recognition Theorygrt-package grt
Extract 'glc' or 'gcjc' coefficientscoef.gcjc coef.glc coef.glcStruct
Calculate d' (d-prime)dprime dprimef
extractAIC method for class 'glc', 'gqc', 'gcjc', and 'grg'extractAIC.gcjc extractAIC.glc extractAIC.gqc extractAIC.grg
Draw a gray-scale Gabor PatchgaborPatch
General Conjunctive Classifiergcjc print.gcjc
General Conjunctive Classifier structuregcjcStruct
General Linear Classifierglc print.glc
General Linear Classifier structureglcStruct
General Quadratic Classifiergqc print.gqc
General Quadratic Classifier structure.gqcStruct
General Random Guessing modelgrg
Obtain means of two multivariate normal populations satisfying certain criteriagrtMeans
Sample from multiple multivariate normal distributionsgrtrnorm
Linear Decision Boundldb
Probability of correct classification based on the optimal linear decision bound.ldb.p.correct
lines Method for class 'gqc'lines.gqcStruct
Log-Likelihood of a 'glc' or 'gcjc' ObjectlogLik.gcjc logLik.glc
Log-Likelihood of a 'glcStruct' or 'gcjcStruct' ObjectlogLik.gcjcStruct logLik.glcStruct
Log-Likelihood of a 'gqc' ObjectlogLik.gqc
Log-Likelihood of a 'gqcStruct' ObjectlogLik.gqcStruct
Calculate sample means and covariance(s) of multivariate datamcovs mcovs.default mcovs.formula
Convert 'new' to 'old' glcStruct formatangle2cart new2old_par
Convert 'old' to 'new' glcStruct formatcart2angle old2new_par
Plot Method for Class 'gcjc'plot.gcjc
Plot Method for Class 'glc'plot.glc
plot Method for Class 'gqc'plot.gqc
plot3d Method for Class 'glc'plot3d.glc
plot3d Method for Class 'gqc'plot3d.gqc
predict method for General Linear Classifierpredict.glc
Quadratic Decision Boundqdb
the proportion correct of the quadratic decision boundary.qdb.p.correct
Scale method for the class 'glc' and 'gqc'scale scale.glc scale.gqc
Sample dataset of a categorization experiment with 1D stimuli.subjdemo_1d
Sample dataset of a categorization experiment with 2D stimuli.subjdemo_2d
Sample dataset of a categorization experiment with 3D stimuli.subjdemo_3d
Sample dataset of a categorization experiment with 2D conjunctive stimuli.subjdemo_cj
Un-scale or re-center the scaled or centered Matrix-like objectunscale