Changes in version 2.0.0 (2026-02-03)                  

Major Changes & Breaking Updates

  - New Default Behavior: Data centering (center = TRUE) is now the
    default. This ensures that the intercept is not penalized in ridge
    regression, aligning the package with standard statistical
    methodologies.
  - API Cleanup: Removed the verbose argument from grid.search. The new
    C++ backend evaluates the lambda grid analytically, rendering
    progress bars unnecessary.
  - Refined Object Inheritance: For lm and glm methods, subset and
    na.action are now handled by the model object prior to
    cross-validation, ensuring consistency with the original model fit.

Performance & Engine Overhaul

The engine has been transitioned from RcppEigen to RcppArmadillo,
allowing the package to leverage high-performance LAPACK and BLAS
libraries for large-scale matrix operations.

  - SVD-Powered Grid Search: grid.search has been entirely rewritten in
    C++. It now utilizes a single Singular Value Decomposition (SVD) to
    evaluate the entire $\lambda$ grid analytically.
      - Efficiency: Reduces computational complexity from $O(np^2)$ per
        grid point to $O(\min(n, p))$ after the initial decomposition.
  - Parallel Computation: Refined and further integrated RcppParallel to
    distribute workloads.
      - For K-fold CV, threads are distributed across folds.
      - For GCV/LOOCV grid searches, threads are distributed across the
        $\lambda$ grid.
  - Optimized LOOCV/GCV: Implemented closed-form solutions for
    Leave-One-Out and Generalized Cross-Validation using the hat-matrix
    diagonal, avoiding $n$ model refits.

Numerical Robustness

  - OLS Evolution: Transitioned from Column-Pivoted QR decomposition to
    Complete Orthogonal Decomposition (COD). This enables the
    computation of the unique minimum $L_2$ norm solution for column
    rank-deficient or underdetermined ($p > n$) systems.
  - Ridge Evolution: Transitioned from Cholesky-based methods to
    Singular Value Decomposition (SVD). This avoids the numerical risks
    associated with forming the cross-product matrix $X^TX$ and ensures
    stability in ill-conditioned settings.
  - Precision Control: Added a tol (tolerance) parameter to define the
    threshold for numerical rank estimation during COD and SVD
    operations.

Internal Improvements

  - Template Metaprogramming: Re-engineered core logic to utilize
    generic, templated C++ code, shifting significant computational
    evaluation to compile-time and reducing runtime overhead.
  - C++17 Migration: Upgraded the package build standard to C++17,
    enabling more expressive syntax and modern compiler optimizations.
  - Memory Optimization: Refactored multi-threaded workers to utilize
    pre-allocated buffers. By eliminating heap allocations within "hot
    loops" (specifically during data training and out-of-sample
    evaluation), the engine achieves significantly higher throughput and
    lower latency.
  - Armadillo Expression Tuning: Optimized the use of Armadillo
    expression templates to maximize lazy evaluation. This minimizes the
    creation of temporary objects and allows the compiler to generate
    more efficient SIMD-augmented computation loops.
  - Comprehensive Testing Suite:
      - R Integration: Implemented extensive testthat suites to validate
        cvLM and grid.search against manual matrix algebra and
        established packages like boot.
      - Numerical Validation: Tests specifically target edge cases
        including ill-conditioned, rank-deficient, and high-dimensional
        ($p > n$) datasets.
  - Zero-Copy Interoperability: Utilizes Armadillo’s advanced memory
    mapping to interface directly with R-allocated memory, ensuring
    zero-copy data passing between R and C++.