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An implementation of maximum likelihood estimators for a variety of heavy tailed distributions, including both the discrete and continuous power law distributions. Additionally, a goodness-of-fit based approach is used to estimate the lower cut-off for the scaling region.

copied from cf-staging / r-powerlaw
Type Size Name Uploaded Downloads Labels
conda 3.0 MB | noarch/r-powerlaw-0.70.6-r42hc72bb7e_3.conda  1 year and 6 months ago 1334 main
conda 3.0 MB | noarch/r-powerlaw-0.70.6-r43hc72bb7e_3.conda  1 year and 6 months ago 1552 main
conda 3.2 MB | noarch/r-powerlaw-0.70.6-r41hc72bb7e_2.tar.bz2  2 years and 2 months ago 1876 main
conda 3.2 MB | noarch/r-powerlaw-0.70.6-r42hc72bb7e_2.tar.bz2  2 years and 2 months ago 1959 main
conda 3.2 MB | noarch/r-powerlaw-0.70.6-r41hc72bb7e_1.tar.bz2  3 years and 7 months ago 3278 main
conda 3.2 MB | noarch/r-powerlaw-0.70.6-r40hc72bb7e_1.tar.bz2  3 years and 7 months ago 2885 main
conda 3.2 MB | noarch/r-powerlaw-0.70.6-r40h6115d3f_1.tar.bz2  4 years and 8 months ago 3443 main
conda 3.1 MB | noarch/r-powerlaw-0.70.6-r36h6115d3f_1.tar.bz2  4 years and 8 months ago 3220 main
conda 3.1 MB | noarch/r-powerlaw-0.70.6-r36h6115d3f_0.tar.bz2  4 years and 8 months ago 2862 main
conda 3.2 MB | noarch/r-powerlaw-0.70.6-r35h6115d3f_0.tar.bz2  4 years and 8 months ago 2953 main

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