# Description of the program: boxcount

This program estimates the Renyi entopy of Qth order using a partition of the embedding space instead of using the Grassberger-Procaccia scheme. I should mention that the memory requirement does not increase exponentially like 1/epsilonM but only like M*(length of series). So it can also be used for small epsilon and large M.
No finite sample corrections are implemented so far.

## Usage:

boxcount [Options]

Everything not being a valid option will be interpreted as a potential datafile name. Given no datafile at all, means read stdin. Also - means stdin

Possible options are:

Option | Description | Default
--- | --- | ---
-l# | number of data points to be used | whole file
-x# | number of lines to be ignored | 0
-c# | column to be read | 1
-d# | delay for the delay vectors | 1
-M# | maximal embedding dimension | 10
-Q# | Order of the entropy | 2.0
-E# | maximal length scale | 1 (data are rescaled to [0:1])
-e# | minimal length scale | 1e-3 (data are rescaled to [0:1])
-## | number of epsilon values | 20
-o[#] | output file name | 'datafile'.box (or if data were read from stdin: stdin.box)
-V# | verbosity level 0: only panic messages 1: add input/output messages | 1
-h | show these options | none

## Description of the Output:

The output file contains four columns for each embedding dimension from 1 to M and for each epsilon value:

1. epsilon (in rescaled units)
2. epsilon (in natural units)
3. Qth order entropy (HQ(dimension,epsilon))
4. Qth order differential entropy ( HQ(dimension,epsilon)-HQ(dimension-1,epsilon))

The slope of the third line gives an estimate of DQ.

View the C-source.

See also d2, c2naive, and c1

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