To explore the uncertainity in the model fit, this
  package provides a <code>bootstrap</code> function. [object Object],[object Object] The
  <code>bootstrap_moby</code> values correspond to the first row
  of table 6.1 in the Clauset et al paper: [object Object],[object Object]<p></p><p>  The <code>bootstrap_p_moby</code> gives the p-value for the
  hypothesis test of whether the data follows a power-law.
  For this simulation study, we get a value of 0.43 (the
  paper gives 0.49).</p>

datasets

bootstrap_moby

This package implements both the discrete and continuous
        maximum likelihood estimators for fitting the power-law
        distribution to data.  Additionally, a goodness-of-fit based
        approach is used to estimate the lower cut-off for the scaling
        region.

Gillespie Colin

poweRlaw

Fitting heavy tailed distributions: the poweRlaw package

bootstrap_moby function

docType

format

M. E. J. Newman, "Power laws, Pareto distributions and
  Zipf's law." Contemporary Physics 46, 323 (2005). See
  http://tuvalu.santafe.edu/~aaronc/powerlaws/data.htm for
  further details.

bootstrap_moby: Example bootstrap results for the full Moby Dick data set

Description

Arguments

docType

format

source

See Also