I am interested in calculating the strength between random variables. I found that the maximal information coefficient is one of the good methods to use and it is robust to the mutual information method. However, I need to calculate the conditional maximal information coefficient. However, based on my search, I found nothing regarding the maximal information coefficient. Is there is an R package that can do this calculation? or any published article regarding this point?


1 Answer 1


Mutual information is well known, sklearn has a good implementation here and in R package entropy.

Regarding MIC, MICtools and minerva are Python/R good implementations. See references given in MICtools repo description for further papers.

Edit: This is a dummy example how to compute MIC score for $x$ and $y$ conditionally given that $z=1$.

from minepy import MINE
import numpy as np
mine = MINE(alpha=0.6, c=15) 


x = np.random.random(100)
y = np.random.random(100)
z = np.random.binomial(1, 0.5, 100)

condition_z_is_one = np.where(z > 0)[0]

mic_score = mine.mic() # 0.21421355042023246
  • $\begingroup$ Thanks a lot for your help. It is appreciated. However, these packages did not count for conditional (the strength between x and y given z. $\endgroup$
    – Maryam
    Commented Jun 21, 2021 at 9:55
  • 4
    $\begingroup$ You could condition x and y simultaneously given set of z values, before passing to these implementations. Meaning we use the subset of x and y given z condition. $\endgroup$ Commented Jun 21, 2021 at 9:59
  • 2
    $\begingroup$ Could you please show me an example to do so and update your answer in order to accept it? As my problem is within the conditional case. $\endgroup$
    – Maryam
    Commented Jun 21, 2021 at 11:26
  • $\begingroup$ @Maryam One dummy example is added using minepy but using mictools is recommended for production. $\endgroup$ Commented Jun 21, 2021 at 15:33

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