Changes in version 0.1.4                        

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.