seekshreyas

from scipy.stats import t


def retroDesign(A, s, alpha=0.05, df=1000000, n_sims=10000):
 """

 :param A: the hypothesized true effect size
 :param s: standard error
 :param alpha: confidence level
 :param df: degree of freedom

seekshreyas

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)

seekshreyas

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