In addition to the basic statistics functions described below, there are several packages for computing statistics on tensor and table data:
• metric computes similarity / distance metrics for comparing two tensors, and associated distance / similarity matrix functions.
• cluster implements agglomerative clustering of items based on metric distance / similarity matrix data.
• convolve convolves data (e.g., for smoothing).
• glm fits a general linear model for one or more dependent variables as a function of one or more independent variables. This encompasses all forms of regression.
• histogram bins data into groups and reports the frequency of elements in the bins.
Stats
The standard statistics functions supported are enumerated in stats/stats.Stats, and include things like Mean, Variance, etc.
You can see that the stats on n-dimensional data are automatically computed across the row (outer-most) dimension. You can reshape your data and the results as needed to get the statistics you want.
Grouping and stats
The stats package has functions that group values in a tensor or a table so that statistics can be computed across the groups. The grouping uses tensorfs to organize the groups and statistics, as in the following example: