NEWS: SmoothPLS
SmoothPLS 0.1.4 (2026-04-09)
- 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.