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• Declarative Data Visualization for Java

Bug fixes.

1. Minor bug fixes.
2. Improve flaky tests.

1. Remove XStream dependency as it exposes many vulnerabilities
2. Bug fixes with ICA, MCC, Shap Value, etc.

1. Switch to dual license model to meet open-source projects (GPL) and the development and distribution needs of commercial distributors (such as OEMs, ISVs and VARs).
2. Java Module friendly with auto module name
3. Redesigned feature engineering packages (missing value imputation, transform, selection, extraction, importance)
4. One-class SVM
5. Isolation forest
6. Feature Hashing
7. One-way ANOVA
8. BigMatrix supporting more than 2 billion elements
9. Latin hypercube sampling
10. CLI supports training, batch prediction, endpoint, etc.
11. Bug fixes

• Spark integration (thanks Pierre Nodet)
• t-SNE is 6X faster (thanks Brault Olivier-O)
• Fully redesigned Gaussian Process Regression with HPO
• L-BFGS-B
• Matern kernel and composed kernels
• Fully redesigned model validation facilities and metrics
• Various optimization and bug fixes

1. enhance MLP
2. bug fixes.

1. AR and ARMA for time series modeling
2. Optimize interpolation package
3. Optimize matrix decomposition memory usage.
4. Bug fixes.

• Generalized ridge regression
• Enhance JSON library
• Do NOT transparently include OpenBLAS library to save space. If the users need optimized matrix computation, they should add the dependency based on their platform. See README for details.

1. New matrix design
2. New formula design
3. Generalized linear models (GLM)
4. Sparse logistic regression
5. Bug fixes