Add mode method for DiscreteWeibull, LogitNormal, Hypergeometric and SkewNormal

[Advaitgaur004]

Description

This PR targeted #604 and work is in progress.

Implemented Modes

• Discreteweibull
• logitnormal
• hypergeometric
• SkewNormal

[Advaitgaur004]

• Binomial Distribution:
  Becomes bimodal when n is large and p is 0.5
• Beta Distribution:
  Becomes bimodal when both shape parameters (α and β) are less than 1
• Mixture Distribution:
  Explicitly created by combining two Gaussian distributions
• Beta-Binomial Distribution:
  Bimodality occurs with small α and β parameters

@rohanbabbar04 These need to be changed if we are opting to send a tuple for multimodal purposes.

[Advaitgaur004]

@aloctavodia Issues have been updated for discrete_weibull; I will use minimize_scale from scipy.optimize since direct calculation for mode in discrete_weibull isn't feasible. For the rest, the mode is calculated as provided in Wikipedia or some reliable sources.

[aloctavodia]

@Advaitgaur004 are you still working on this?

[Advaitgaur004]

@Advaitgaur004 are you still working on this?

Yes, i am busy for 2 more days, i'll made pr in some time

[aloctavodia]

great! Thanks

[Advaitgaur004]

Changes Made in

Discreteweibull
Exgaussian
logitnormal
hypergeometric
SkewNormal

[aloctavodia]

@Advaitgaur004 check the tests that are failing, you will see linter errors.

[Advaitgaur004]

@Advaitgaur004 check the tests that are failing, you will see linter errors.

I've finished reviewing the files. Now I see the issue with those three files; I didn't apply the black style, which caused the problem. I have now updated all the files accordingly, and there are no linter errors.