GH-8487: crafting HGLM parameters.
GH-8487: implement EM algo.
GH-8487: forming the fixed matrices and vectors.
GH-8487: add test to make sure correct initialization of fixed, random coefficients, sigma values and T matrix.
GH-8487: Finished implementing EM to estimate fixed coefficients, random coefficients, tmat and tauEVar
GH-8487: finished implementing prediction but still need to figure out the model metrics calculation.
GH-8487: Adding support for models without random intercept.
GH-8487: adding normalization and denormalization of coefficients for fixed and random.
GH-8487: Completed prediction implementation and added tests to make sure prediction is correct when standardize=true/false, random_intercept = true/false.
GH-8487: fixing model metric classes.
GH-8487: add python and R tests.
GH-8487: adding hooks to generate synthetic data.
GH-8487: added scoring history, model summary, coefficient tables.
GH-8487: added modelmetrics for validation frame.
GH-8487: From experiment to find best tauEVar calculation process.  The one in equation 10 is best.
GH-8487: add capability in Python client to extract  scoring history, model summary, model metrics, model coefficients (fixed and random), icc, T matrix, residual variance.
GH-8487: done checking scoring history, model summary and model metrics.
GH-8487: added R client test for utility functions.
GH-8487: use lambda_ instead lf Lambda in pyunit_benign_glm.py
GH-8487: remove standardize from HGLM as the convention does not do standardization.

Co-authored-by: Veronika Maurerová <[email protected]>

Move test to check init values are set correctly to Python from Java.  I was not able to find a good combination of initial betas/ubetas and t matrix to make it work.