Distfit: Probability density fitting
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Background
distfit is a python package for probability density fitting across 89 univariate distributions to non-censored data by residual sum of squares (RSS), and hypothesis testing.
Probability density fitting is the fitting of a probability distribution to a series of data concerning the repeated measurement of a variable phenomenon. distfit scores each of the 89 different distributions for the fit wih the empirical distribution and return the best scoring distribution.
Functionalities
The distfit library is created with classes to ensure simplicity in usage.
# Import library
from distfit import distfit
dist = distfit() # Specify desired parameters
dist.fit_transform(X) # Fit distributions on empirical data X
dist.predict(y) # Predict the probability of the resonse variables
dist.plot() # Plot the best fitted distribution (y is included if