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from pylab import *
from scipy.stats import uniform, binom, expon, beta
true_gamma = 0.5
N = 600
T = 15
data = zeros((2, N), dtype=float)
event_times = data[0,:]
event_times[:] = uniform(0,15).rvs(N)
@ricardoV94
ricardoV94 / dunn.py
Last active July 24, 2017 11:22 — forked from alimuldal/dunn.py
Implementation of Dunn's multiple comparison test, following a Kruskal-Wallis 1-way ANOVA
import numpy as np
from scipy import stats
from itertools import combinations
from statsmodels.stats.multitest import multipletests
from statsmodels.stats.libqsturng import psturng
import warnings
def kw_dunn(groups, to_compare=None, alpha=0.05, method='bonf'):
"""