Agglomerate[{e_1, e_2, ...}] gives an hierarchical clustering of the elements e_1, e_2, ....Agglomerate[{e_1 -> v_1, e_2 -> v_2, ...}] represents e_i with v_i in each ...

HermiteH[n, x] gives the Hermite polynomial H_n (x).

There are a number of features of the GUIKit framework that aid deployment of user interface definitions with your own AddOns so that they can be easily executed when needed. ...

A wide variety of plots and charts are used to gain an overview of data from a statistical perspective. Some summarize statistical computations on the data, while others ...

If you make a definition like f[x_]:=x Sin[x], Mathematica will store the expression x Sin[x] in a form that can be evaluated for any x. Then when you give a particular value ...

BarChart3D[{y_1, y_2, ...}] makes a 3D bar chart with bar lengths y_1, y_2, ....BarChart3D[{..., w_i[y_i, ...], ..., w_j[y_j, ...], ...}] makes a 3D bar chart with bar ...

BubbleChart3D[{{x_1, y_1, z_1, u_1}, {x_2, y_2, z_2, u_2}, ...}] makes a 3D bubble chart with bubbles at positions {x_i, y_i, z_i} with sizes u_i.BubbleChart3D[{..., ...

Plots of data based on measurements often have vertical lines or intervals centered at the points to indicate the associated error estimates. Mathematica lets you add such ...

InternallyBalancedDecomposition[ss] yields the internally balanced decomposition of the StateSpaceModel object ss. The result is a list {s, ib} where s is the similarity ...

JordanModelDecomposition[ss] yields the Jordan decomposition of a StateSpaceModel object ss. The result is a list {s, jc} where s is a similarity matrix and jc is the Jordan ...