aat_bootstrap | Compute bootstrapped approach-bias scores |

aat_compute | Compute simple AAT scores |

aat_covreliability | Compute a dataset's reliability from its covariance matrix |

aat_covreliability_jackknife | Compute a dataset's reliability from its covariance matrix |

aat_doublemeandiff | AAT score computation algorithms |

aat_doublemediandiff | AAT score computation algorithms |

aat_dscore | AAT score computation algorithms |

aat_dscore_multiblock | AAT score computation algorithms |

aat_getstudydata | Simulate AAT datasets and predict parameters |

aat_mediandscore | AAT score computation algorithms |

aat_regression | AAT score computation algorithms |

aat_simulate | Simulate AAT datasets and predict parameters |

aat_simulate2 | Simulate AAT datasets and predict parameters |

aat_singlemeandiff | AAT score computation algorithms |

aat_singlemediandiff | AAT score computation algorithms |

aat_splithalf | Compute the bootstrapped split-half reliability for approach-avoidance task data |

aat_standardregression | AAT score computation algorithms |

aat_stimulusscores | Compute stimulus-specific bias scores Computes mean single-difference scores (push - pull) for each stimulus. |

aat_stimulus_rest | Compute stimulus-rest correlations of double-difference scores This function provides a statistic that can give an indication of how deviant the responses to specific stimuli are, in comparison to the rest of the stimulus set. The algorithm computes stimulus-rest correlations of stimulus-specific double-difference scores. It takes single-difference approach-avoidance scores for each stimulus, and computes every possible subtraction between individual stimuli from both stimulus categories. It then computes correlations between every such subtraction of stimuli on one hand, and the mean double difference score of all other stimuli. Stimulus-rest correlations are then computed by averaging every such subtraction-rest correlation involving a specific stimulus. |

Algorithms | AAT score computation algorithms |

calpha | Covariance Matrix-Based Reliability Coefficients |

case_prune_3SD | Pre-processing rules |

compcorr | Correlation tools |

cormean | Compute a minimally biased average of correlation values |

correlation-tools | Correlation tools |

covEM | Covariance matrix computation with multiple imputation |

covrel | Covariance Matrix-Based Reliability Coefficients |

erotica | AAT examining approach bias for erotic stimuli |

error_prune_dropcases | Pre-processing rules |

error_replace_blockmeanplus | Pre-processing rules |

FlanaganRulon | Split Half-Based Reliability Coefficients |

lambda2 | Covariance Matrix-Based Reliability Coefficients |

lambda4 | Covariance Matrix-Based Reliability Coefficients |

multiple.cor | Multiple correlation Computes the multiple correlation coefficient of variables in 'ymat' with the variable 'x' |

partial.cor | Partial correlation Compute the correlation between x and y while controlling for z. |

plot.aat_bootstrap | Compute bootstrapped approach-bias scores |

plot.aat_covreliability_jackknife | Compute a dataset's reliability from its covariance matrix |

plot.aat_splithalf | Compute the bootstrapped split-half reliability for approach-avoidance task data |

plot.aat_stimulus_rest | Compute stimulus-rest correlations of double-difference scores This function provides a statistic that can give an indication of how deviant the responses to specific stimuli are, in comparison to the rest of the stimulus set. The algorithm computes stimulus-rest correlations of stimulus-specific double-difference scores. It takes single-difference approach-avoidance scores for each stimulus, and computes every possible subtraction between individual stimuli from both stimulus categories. It then computes correlations between every such subtraction of stimuli on one hand, and the mean double difference score of all other stimuli. Stimulus-rest correlations are then computed by averaging every such subtraction-rest correlation involving a specific stimulus. |

plot.qreliability | Compute psychological experiment reliability |

Preprocessing | Pre-processing rules |

print.aat_bootstrap | Compute bootstrapped approach-bias scores |

print.aat_covreliability | Compute a dataset's reliability from its covariance matrix |

print.aat_covreliability_jackknife | Compute a dataset's reliability from its covariance matrix |

print.aat_splithalf | Compute the bootstrapped split-half reliability for approach-avoidance task data |

print.qreliability | Compute psychological experiment reliability |

prune_nothing | Pre-processing rules |

q_reliability | Compute psychological experiment reliability |

q_reliability2 | Compute psychological experiment reliability |

r2p | Correlation tools |

r2t | Correlation tools |

r2z | Correlation tools |

RajuCoefficient | Split Half-Based Reliability Coefficients |

rconfint | Correlation tools |

SpearmanBrown | Split Half-Based Reliability Coefficients |

splitrel | Split Half-Based Reliability Coefficients |

trial_prune_3MAD | Pre-processing rules |

trial_prune_3SD | Pre-processing rules |

trial_prune_grubbs | Pre-processing rules |

trial_prune_percent_sample | Pre-processing rules |

trial_prune_percent_subject | Pre-processing rules |

trial_prune_SD_dropcases | Pre-processing rules |

trial_recode_SD | Pre-processing rules |

z2r | Correlation tools |