Fit Bayesian Ridge Regression models to obtain regression outputs with Bayesian posterior standard deviations and precision parameters for quantitative analysis.

Fit a Gaussian Hidden Markov Model to discover hidden regimes in data, returning state sequences, transition matrices, and Gaussian parameters for unsupervised pattern analysis.

Fit a linear regression model to analyze relationships in financial data, returning coefficients and residuals for quantitative research.

Fit a Random Forest Regressor to analyze financial data, returning results with feature importances for quantitative modeling and research.

Fit a Support Vector Regressor model for financial data analysis and quantitative research, returning detailed results for predictive modeling.

Fit an XGBoost Regressor for financial modeling and return results with feature importances to analyze predictive factors in quantitative research.