ExpGammaDistribution[\[Kappa], \[Theta], \[Mu]] represents an exp-gamma distribution with shape parameter \[Kappa], scale parameter \[Theta], and location parameter \[Mu].
FourierDCT[list] finds the Fourier discrete cosine transform of a list of real numbers.FourierDCT[list, m] finds the Fourier discrete cosine transform of type m.
FourierDST[list] finds the Fourier discrete sine transform of a list of real numbers.FourierDST[list, m] finds the Fourier discrete sine transform of type m.
GatherBy[list, f] gathers into sublists each set of elements in list that gives the same value when f is applied.GatherBy[list, {f_1, f_2, ...}] gathers list into nested ...
InverseFourier[list] finds the discrete inverse Fourier transform of a list of complex numbers.
LogGammaDistribution[\[Alpha], \[Beta], \[Mu]] represents a log-gamma distribution with shape parameters \[Alpha] and \[Beta] and location parameter \[Mu].
MultinomialDistribution[n, {p_1, p_2, ..., p_m}] represents a multinomial distribution with n trials and probabilities p_i.
Raster
(Built-in Mathematica Symbol) Raster[{{a_11, a_12, ...}, ...}] is a two-dimensional graphics primitive which represents a rectangular array of gray cells. Raster[{{{r_11, g_11, b_11}, ...}, ...}] ...
WakebyDistribution[\[Alpha], \[Beta], \[Gamma], \[Delta], \[Mu]] represents Wakeby distribution with shape parameters \[Beta] and \[Delta], scale parameters \[Alpha] and ...
FindFit
(Built-in Mathematica Symbol) FindFit[data, expr, pars, vars] finds numerical values of the parameters pars that make expr give a best fit to data as a function of vars. The data can have the form {{x_1, ...