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NEWS: SmoothPLS

SmoothPLS 0.1.4 (2026-04-09)

Performance Improvements

• Implemented dynamic load balancing for parallel computing in evaluate_lambda functions.
• Numerical integration steps are now up to 8x faster on multi-core machines while preventing RAM overhead on massive datasets (e.g. >100.000 evaluations).

SmoothPLS 0.1.3 (2026-04-09)

Improvements

• Documentation:
  • Launched the official pkgdown website (hosted on GitHub Pages) including detailed vignettes and function references.
  • Added the complete compiled PDF manual to inst/doc/.
• README & Branding:
  • Added a comprehensive quick-start example (One-State Categorical PLS) with visual outputs.
  • Fixed LaTeX equations rendering for cross-compatibility between GitHub and Pandoc/pkgdown.
  • Added institutional links (Decathlon SportsLab, Inria).
• Continuous Integration:
  • Configured GitHub Actions workflows for automated R CMD check and pkgdown site deployment.

SmoothPLS 0.1.2 (2026-04-08)

Core Improvements & Stability

• Numerical Precision: Optimized categorical integration in evaluate_id_func_integral with stricter relative tolerance (rel.tol) and increased subdivisions (1000) for high-order B-splines.
• Analytic Prediction: Implemented analytic L2 inner product for Scalar Functional Data (SFD) using fda::inprod, replacing discrete trapezoidal integration for near-perfect precision.
• Safety Checks: Added time-range assertions in smoothPLS_predict to prevent silent errors when predicting on data outside the basis domain.

Bug Fixes & Refactoring

• Tidyselect Compatibility: Fixed deprecation warnings by implementing all_of() in data pivoting functions.
• Multivariate Support: Corrected logical assertions in smoothPLS to properly handle mixed lists of categorical and numerical predictors.
• Integration Robustness: Added stop.on.error = FALSE in segment integration to handle micro-intervals without crashing the full model.

Testing

• Core Test Suite: Added 70 unit tests covering Theorems (univariate equivalence), score orthogonality, and prediction consistency.
• Edge Cases: Added tests for time-mismatch handling and multi-state categorical transitions.

SmoothPLS 0.1.1 (2026-03-20)

Improvements

• Code Refactoring: Modularization of internal functions for Lambda matrix evaluation.
• Synthetic Data: Improved generate_X_df and generate_Y_df for more realistic categorical state transitions.
• S3 Structure Prep: Initial work on internal objects to support future S3 methods (print, plot, predict).

SmoothPLS 0.1.0 (2025-12-15)

Initial Release

• Thesis Milestone: First functional version used for the initial examples in the doctoral thesis.
• Core Algorithms: Implementation of Smooth PLS for Hybrid Functional Data (CFD and SFD).
• Basis Expansion: Support for B-spline basis representation of functional predictors.
• Categorical Handling: Implementation of the “active area” integration concept for state-based predictors.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.