SmoothKernelDistribution[{x_1, x_2, ...}] represents a smooth kernel distribution based on the data values x_i.SmoothKernelDistribution[{{x_1, y_1, ...}, {x_2, y_2, ...}, ...
StationaryWaveletPacketTransform[data] gives the stationary wavelet packet transform (SWPT) of an array of data.StationaryWaveletPacketTransform[data, wave] gives the ...
LogitModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a binomial logistic regression model of the form 1/(1 + E -(\[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + \ ...)) ...
MaxStableDistribution[\[Mu], \[Sigma], \[Xi]] represents a generalized maximum extreme value distribution with location parameter \[Mu], scale parameter \[Sigma], and shape ...
MinStableDistribution[\[Mu], \[Sigma], \[Xi]] represents a generalized minimum extreme value distribution with location parameter \[Mu], scale parameter \[Sigma], and shape ...
ProbabilityPlot[list] generates a plot of the CDF of list against the CDF of a normal distribution.ProbabilityPlot[dist] generates a plot of the CDF of the distribution dist ...
ProbitModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a binomial probit regression model of the form 1/2 (1 + erf((\[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + \ ...
ListLinePlot[{y_1, y_2, ...}] plots a line through a list of values, assumed to correspond to x coordinates 1, 2, .... ListLinePlot[{{x_1, y_1}, {x_2, y_2}, ...}] plots a ...
OrderStarSymbolSize is an option to OrderStarPlot that specifies the size of the symbols used to represent poles, zeros and interpolation points.
OrderStarSymbolThickness is an option to OrderStarPlot that specifies the thickness of the outline of the symbols used to represent interpolation points, poles and zeros.