Data from: A generalized distribution interpolated between the exponential and power law distributions and applied to pill bug (Armadillidium vulgare) walking data

The walking pattern of an organism is typically designated as either a
Lévy walk or a Brownian walk based on whether the frequency distribution
of its linear step lengths follows a power law distribution or an
exponential distribution. However, there are many cases where actual data
cannot be classified into either of these categories. In this paper, we
propose a general distribution that includes the power law and exponential
distributions as special cases. This distribution has two parameters: one
parameter represents the exponent, similar to the power law and
exponential distributions, and the other is a shape parameter representing
the shape of the distribution. By introducing this distribution, an
intermediate distribution model can be interpolated between the power law
and exponential distributions. In this study, the proposed distribution
was fitted to the frequency distribution of the step length calculated
from the walking data of pill bugs. The autocorrelation coefficients were
also calculated from the time-series data of the step length, and the
relationship between the shape parameter and time dependency was
investigated. The results indicate that individuals whose step length
frequency distributions are closer to the power law distribution have
stronger time dependence. C++ program for parameter estimation of
generalized distributions and source code for statistical
analysis using R.