NAME

       plfit - fits power-law distributions to empirical data

SYNOPSIS

       plfit [OPTIONS] [infile ...]

DESCRIPTION

       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.

OPTIONS

       -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