An object containing all data necessary for preference elicitation.
A matrix or dataframe of data.
A list of functions that give the prior on each variable.
A scalar value to use for the confusion factor (default 0.1).
(Internal use only) A matrix of sigma * diag(ncol(data)).
A list of lists of preferences. For each element x, x[] > x[].
A list of lists of indifferences. For each element x, x[] = x[].
A vector of weights determined by the inference algorithm.
Adds a preference created using %>%, %<%, or %=%.
infer(estimate = "recommended")
Calls the “infer” function to guess weights
Calculates the utilty of each row in our dataset
suggest(maxComparisons = 10)
Calls the “suggest” function to guess weights
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