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>

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.

Colin Gillespie

poweRlaw

Fitting heavy tailed distributions: the poweRlaw package

bootstrap_moby function

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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

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format

source

See Also