DamerauLevenshteinDistance[u, v] gives the Damerau\[Dash]Levenshtein distance between strings or vectors u and v.
JaccardDissimilarity[u, v] gives the Jaccard dissimilarity between Boolean vectors u and v.
ManhattanDistance[u, v] gives the Manhattan or "city block" distance between vectors u and v.
MatchingDissimilarity[u, v] gives the matching dissimilarity between Boolean vectors u and v.
NormalizedSquaredEuclideanDistance[u, v] gives the normalized squared Euclidean distance between vectors u and v.
EstimatedDistribution[data, dist] estimates the parametric distribution dist from data.EstimatedDistribution[data, dist, {{p, p_0}, {q, q_0}, ...}] estimates the parameters ...
GeneralizedLinearModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a generalized linear model of the form g -1 (\[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + ...) that ...
ListDensityPlot[array] generates a smooth density plot from an array of values. ListDensityPlot[{{x_1, y_1, f_1}, {x_2, y_2, f_2}, ...}] generates a density plot with values ...
LocationEquivalenceTest[{data_1, data_2, ...}] tests whether the means or medians of the data_i are equal. LocationEquivalenceTest[{data_1, ...}, " property"] returns the ...
Histogram[{x_1, x_2, ...}] plots a histogram of the values x_i.Histogram[{x_1, x_2, ...}, bspec] plots a histogram with bin width specification bspec.Histogram[{x_1, x_2, ...