DavisDistribution[b, n, \[Mu]] represents a Davis distribution with scale parameter b, shape parameter n, and location parameter \[Mu].

ExponentialPowerDistribution[\[Kappa], \[Mu], \[Sigma]] represents an exponential power distribution with shape parameter \[Kappa], location parameter \[Mu], and scale ...

FindEulerianCycle[g] finds an Eulerian cycle in the graph g if one exists.FindEulerianCycle[g, k] finds at most k Eulerian cycles.

FourierSequenceTransform[expr, n, \[Omega]] gives the Fourier sequence transform of expr.FourierSequenceTransform[expr, {n_1, n_2, ...}, {\[Omega]_1, \[Omega]_2, ...}] gives ...

HeavisideTheta[x] represents the Heaviside theta function \[Theta](x), equal to 0 for x < 0 and 1 for x > 0. HeavisideTheta[x_1, x_2, ...] represents the multidimensional ...

HotellingTSquareDistribution[p, m] represents Hotelling's T^2 distribution with dimensionality parameter p and m degrees of freedom.

JordanDecomposition[m] yields the Jordan decomposition of a square matrix m. The result is a list {s, j} where s is a similarity matrix and j is the Jordan canonical form of ...

LinearProgramming[c, m, b] finds a vector x that minimizes the quantity c.x subject to the constraints m.x >= b and x >= 0. LinearProgramming[c, m, {{b_1, s_1}, {b_2, s_2}, ...

LogGammaDistribution[\[Alpha], \[Beta], \[Mu]] represents a log-gamma distribution with shape parameters \[Alpha] and \[Beta] and location parameter \[Mu].

LogLinearPlot[f, {x, x_min, x_max}] generates a log-linear plot of f as a function of x from x_min to x_max. LogLinearPlot[{f_1, f_2, ...}, {x, x_min, x_max}] generates ...