Toolbox for Error-Driven Learning Simulations with Two-Layer Networks

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Documentation for package ‘edl’ version 1.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

activationsCueSet	Calculate the change in activation for a specific cue or set of cues.
activationsEvents	Calculate the activations for each learning event.
activationsMatrix	Calculate the activations for one or a set of cues.
activationsOutcomes	Calculate the activations for all outcomes in the data.
check	Remove empty cues and/or outcomes.
checkWM	Check whether cues and outcomes exist in a weight matrix and optionally add.
createTrainingData	Create event training data from a frequency data frame.
createWM	Create empty weight matrix based on a set of cues and outcomes.
cueWindow	Create a 'cue window', for overlapping or continuous cues.
dat	Simulated learning data.
edl	Toolbox for Error-Driven Learning Simulations with Two-Layer Networks
getActivations	Function to calculate the activations.
getCues	Extract cues from list of weightmatrices.
getLambda	Retrieve the lambda values for all or specific outcomes for each learning event.
getOutcomes	Extract outcomes from list of weightmatrices.
getUpdate	Retrieve the weight updates and their change for each learning event.
getValues	Retrieve all cues from a vector of text strings.
getWeightsByCue	Extract the change of connection weights between a specific cue and all outcomes.
getWeightsByOutcome	Extract the change of connection weights between all cues and a specific outcome.
getWM	Retrieve all cues from a vector of text strings.
luceChoice	Function implementing the Luce choice rule.
plotActivations	Visualize the change of connection weights between a specific outcome and all cues.
plotCueWeights	Visualize the change of connection weights between a specific cue and all outcomes.
plotNetwork	Return strong weights.
plotOutcomeWeights	Visualize the change of connection weights between a specific outcome and all cues.
RWlearning	Function implementing the Rescorla-Wagner learning.
RWlearningMatrix	Function implementing the Rescorla-Wagner learning.
RWlearningNoCueCompetition	Function implementing the Rescorla-Wagner learning equations without cue competition (for illustration purposes).
RWlearningNoOutcomeCompetition	Function implementing the Rescorla-Wagner learning equetions without outcome competition (for illustration purposes).
setBackground	Set value background cue.
updateWeights	Function implementing the Rescorla-Wagner learning for a single learning event.
updateWeightsNoCueCompetition	Function implementing the Rescorla-Wagner learning equations without cue competition for a single learning event.
updateWeightsNoOutcomeCompetition	Function implementing the Rescorla-Wagner learning equations without outcome competition (for illustration purposes) for a single learning event.