plfit - fits power-law distributions to empirical data
plfit [OPTIONS] [infile ...]
Reads data points from each given input file and fits a power-law
distribution to them, one by one, according to the method of Clauset,
Shalizi and Newman. If no input files are given, the standard input will be
processed.
This implementation uses the L-BFGS optimization method to find
the optimal alpha for a given xmin in the discrete case. If you want to use
the legacy brute-force approach originally published in the above paper, use
the -a switch.
- -h
- shows this help message
- -v
- shows version information
- -a RANGE
- use legacy brute-force search for the optimal alpha when a discrete
power-law distribution is fitted. RANGE must be in MIN:STEP:MAX format,
the default is 1.5:0.01:3.5.
- -b
- brief (but easily parseable) output format
- -c
- force continuous fitting even when every sample is an integer
- -D VALUE
- divide each sample in the input data by VALUE to prevent underflows when
fitting discrete power-law distribution
- -e EPS
- try to provide a p-value with a precision of EPS when the p-value is
calculated using the exact method. The default is 0.01.
- -f
- use finite-size correction
- -m XMIN
- use XMIN as the minimum value for x instead of searching for the optimal
value
- -M
- print the first four central moments (i.e. mean, variance, skewness and
kurtosis) of the input data to help assessing the shape of the pdf it may
have come from.
- -p METHOD
- use METHOD to calculate the p-value. Must be one of skip, approximate or
exact. Default is skip.
- -s SEED
- use SEED to seed the random number generator