• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.665-682
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• 2020

This paper introduces numba library in Python, which improves computational efficiency of the provided implemented code written by naive Python language by applying just-in-time (JIT) compilation. To apply just-in-time compilation, the numba only needs to use a decorator on a target Python function. We provide implementation examples with numba for the permutation test and the parameter estimation for Gaussian mixture distribution. We also numerically show the efficiency of numba by comparing the total computation times of the implementation using naive python and the implementation using numba for each application.