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| grt-package | General Recognition Theory |
| angle2cart | Convert 'new' to 'old' glcStruct format |
| cart2angle | Convert 'old' to 'new' glcStruct format |
| coef.gcjc | Extract 'glc' or 'gcjc' coefficients |
| coef.glc | Extract 'glc' or 'gcjc' coefficients |
| coef.glcStruct | Extract 'glc' or 'gcjc' coefficients |
| dprime | Calculate d' (d-prime) |
| dprimef | Calculate d' (d-prime) |
| extractAIC.gcjc | extractAIC method for class 'glc', 'gqc', 'gcjc', and 'grg' |
| extractAIC.glc | extractAIC method for class 'glc', 'gqc', 'gcjc', and 'grg' |
| extractAIC.gqc | extractAIC method for class 'glc', 'gqc', 'gcjc', and 'grg' |
| extractAIC.grg | extractAIC method for class 'glc', 'gqc', 'gcjc', and 'grg' |
| gaborPatch | Draw a gray-scale Gabor Patch |
| gcjc | General Conjunctive Classifier |
| gcjcStruct | General Conjunctive Classifier structure |
| glc | General Linear Classifier |
| glcStruct | General Linear Classifier structure |
| gqc | General Quadratic Classifier |
| gqcStruct | General Quadratic Classifier structure. |
| grg | General Random Guessing model |
| grt | General Recognition Theory |
| grtMeans | Obtain means of two multivariate normal populations satisfying certain criteria |
| grtrnorm | Sample from multiple multivariate normal distributions |
| ldb | Linear Decision Bound |
| ldb.p.correct | Probability of correct classification based on the optimal linear decision bound. |
| lines.gqcStruct | lines Method for class 'gqc' |
| logLik.gcjc | Log-Likelihood of a 'glc' or 'gcjc' Object |
| logLik.gcjcStruct | Log-Likelihood of a 'glcStruct' or 'gcjcStruct' Object |
| logLik.glc | Log-Likelihood of a 'glc' or 'gcjc' Object |
| logLik.glcStruct | Log-Likelihood of a 'glcStruct' or 'gcjcStruct' Object |
| logLik.gqc | Log-Likelihood of a 'gqc' Object |
| logLik.gqcStruct | Log-Likelihood of a 'gqcStruct' Object |
| mcovs | Calculate sample means and covariance(s) of multivariate data |
| mcovs.default | Calculate sample means and covariance(s) of multivariate data |
| mcovs.formula | Calculate sample means and covariance(s) of multivariate data |
| new2old_par | Convert 'new' to 'old' glcStruct format |
| old2new_par | Convert 'old' to 'new' glcStruct format |
| plot.gcjc | Plot Method for Class 'gcjc' |
| plot.glc | Plot Method for Class 'glc' |
| plot.gqc | plot Method for Class 'gqc' |
| plot3d.glc | plot3d Method for Class 'glc' |
| plot3d.gqc | plot3d Method for Class 'gqc' |
| predict.glc | predict method for General Linear Classifier |
| print.gcjc | General Conjunctive Classifier |
| print.glc | General Linear Classifier |
| print.gqc | General Quadratic Classifier |
| qdb | Quadratic Decision Bound |
| qdb.p.correct | the proportion correct of the quadratic decision boundary. |
| scale | Scale method for the class 'glc' and 'gqc' |
| scale.glc | Scale method for the class 'glc' and 'gqc' |
| scale.gqc | Scale method for the class 'glc' and 'gqc' |
| 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. |
| unscale | Un-scale or re-center the scaled or centered Matrix-like object |