An object containing all data necessary for preference elicitation.

Fields

data: A matrix or dataframe of data.
priors: A list of functions that give the prior on each variable.
sigma: A scalar value to use for the confusion factor (default 0.1).
Sigma: (Internal use only) A matrix of sigma * diag(ncol(data)).
strict: A list of lists of preferences. For each element x, x[[1]] > x[[2]].
indif: A list of lists of indifference preferences. For each element x, x[[1]] = x[[2]].
weights: A vector of weights determined by the inference algorithm.

addPref(x): Adds a preference created using %>%, %<%, or %=%.
infer(estimate = "recommended"): Calls the ``infer'' function to guess weights
rank(): Calculates the utility of each row in our dataset
suggest(maxComparisons = 10): Calls the ``suggest'' function to guess weights