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GH-8487: add HGLM as a separate toolbox.
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]>
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