power.law.fit: Fitting a power-law distribution function to discrete data
In igraph/rigraph: Network Analysis and Visualization

power.law.fit

R Documentation

Fitting a power-law distribution function to discrete data

power.law.fit() was renamed to fit_power_law() to create a more consistent API.

power.law.fit(
  x,
  xmin = NULL,
  start = 2,
  force.continuous = FALSE,
  implementation = c("plfit", "R.mle"),
  ...
)

`x`	The data to fit, a numeric vector. For implementation ‘`R.mle`’ the data must be integer values. For the ‘`plfit`’ implementation non-integer values might be present and then a continuous power-law distribution is fitted.
`xmin`	Numeric scalar, or `NULL`. The lower bound for fitting the power-law. If `NULL`, the smallest value in `x` will be used for the ‘`R.mle`’ implementation, and its value will be automatically determined for the ‘`plfit`’ implementation. This argument makes it possible to fit only the tail of the distribution.
`start`	Numeric scalar. The initial value of the exponent for the minimizing function, for the ‘`R.mle`’ implementation. Usually it is safe to leave this untouched.
`force.continuous`	Logical scalar. Whether to force a continuous distribution for the ‘`plfit`’ implementation, even if the sample vector contains integer values only (by chance). If this argument is false, igraph will assume a continuous distribution if at least one sample is non-integer and assume a discrete distribution otherwise.
`implementation`	Character scalar. Which implementation to use. See details below.
`...`	Additional arguments, passed to the maximum likelihood optimizing function, `stats4::mle()`, if the ‘`R.mle`’ implementation is chosen. It is ignored by the ‘`plfit`’ implementation.