Fisher-scoring

- `predict_ci` method with the ability to compute confidence intervals for predicted probabilities, supporting both "logit" and "proba" methods. Both methods are based on the delta method with a slight difference in computation. The `logit` method is adopted from the book "Applied Logistic Regression" and the delta method from this stackoverflow [thread](https://stackoverflow.com/questions/47414842/confidence-interval-of-probability-prediction-from-logistic-regression-statsmode).
- Introduction of model parameters (standard errors, Wald scores, confidence intervals) for `FisherScoringFocalRegression` allowing to use inference about maximum likelihood estimates obtained with this model.

- **FisherScoringMultinomialRegression**: Enhanced flexibility by removing fixed data types for numpy arrays, improving compatibility with diverse datasets.
- **Code Optimization**: Refined matrix operations to maintain peak performance across Fisher Scoring modules.
- **Documentation & Image Display**: Adjusted README formatting and hosted images to ensure accurate display on PyPI, enhancing readability and visual guidance.

Performance Improvements
With optimized matrix calculations, all models now exhibit faster training times and reduced memory usage. The streamlined operations provide up to 290x speed improvements:
- **Multinomial Logistic Regression**: Reduced training time from 125.10s to 0.43s (~290x speedup).
- **Logistic Regression**: Reduced training time from 0.24s to 0.05s (~5x speedup).
- **Focal Loss Logistic Regression**: Reduced training time from 0.26s to 0.01s (~26x speedup).

Fixes and Usability Enhancements
- **Verbose Parameter for Focal Loss**: Fixed the verbose logging for `FisherScoringFocalRegression`, ensuring informative output during model training.
- **Improved Documentation**: Expanded README to provide clear installation instructions, feature descriptions, and example usage for all models, enhancing user experience.
- **External Image Display on PyPI**: Ensured hosted images display properly in the README for a polished PyPI appearance.

This release introduces some changes to the functionality and outputs of logistic regression and correct log likelihood display for multinomial logistic regression model.

Changelog
* Added minor changes to the model code for logistic regression and multinomial logistic regression.

This release introduces enhanced statistical outputs for Multinomial Regression, including the calculation of coefficients, standard errors, p-values, and confidence intervals for each class.

Changelog

- Added the calculation of coefficients, standard errors, p-values, and confidence intervals for each class in Multinomial Regression.

This release includes downgrading dependencies of scikit-learn and numpy for better handling with other packages.

Changelog

- Updated dependency versions