Changes in version 0.8.1 (2025-11-18)                  

  - Fix links in the DESCRIPTION files.

                        Changes in version 0.8.0                        

  - Added DOI of the package.

  - Added high performance PLS Cox backends:
    
      - big_pls_cox_fast() for exact PLS Cox fits on both dense matrices
        and bigmemory::big.matrix objects.
      - big_pls_cox_gd() for gradient based optimisation of Cox partial
        likelihood in the latent PLS space.

  - big_pls_cox_gd() now supports several optimisation schemes via the
    method argument:
    
      - "gd" for a basic fixed step gradient descent,
      - "bb" for a Barzilai Borwein step size,
      - "nesterov" for Nesterov style acceleration,
      - "bfgs" for a quasi Newton update.
    
    All optimisers share the same PLS scores and differ only in how the
    Cox coefficients are updated.

                        Changes in version 0.7.0                        

  - Fixed problem in C code that led to an additional error during CRAN
    tests.

  - Added helpers for big_pls_cox() and big_pls_cox_gd().

  - New prediction helpers:
    
      - predict.big_pls_cox_fast() and predict.big_pls_cox_gd() now
        handle dense matrices, big.matrix inputs and in-sample
        prediction.
      - type = "components" returns the PLS scores for the requested
        components.
      - Arguments comps and coef allow partial use of components and
        user supplied Cox coefficients.

  - Added simple diagnostic accessors for gradient based fits, including
    iteration counts, log-likelihood trajectory, gradient norms and step
    sizes.

                 Changes in version 0.6.0 (2025-11-11)                  

See the "Release highlights" section of the README for a condensed
overview of these changes.

  - Added C++ implementations for Cox deviance residuals with streaming
    support for bigmemory matrices together with benchmarking utilities.
  - Introduced prediction wrappers and component selection helpers
    (AIC/BIC) for big_pls_cox() and big_pls_cox_gd().
  - Enabled naive sparsity control in big_pls_cox() and exposed survival
    model objects for downstream predictions.
  - Added cross-validation helpers cv.big_pls_cox() and
    cv.big_pls_cox_gd() mirroring the plsRcox criteria, including the
    recommended survivalROC iAUC metric by default.
  - Documented the legacy and big-memory prediction helpers with
    runnable examples and cross references to diagnostic utilities.
  - Extended unit test coverage for the new deviance and prediction
    features.
  - Fixed cv.coxgpls() to accept big.matrix predictors without coercion
    errors.

                        Changes in version 0.5.0                        

  - Added reproducible benchmarking utilities under inst/benchmarks
    comparing big_pls_cox() against plsRcox::plsRcox() on in-memory and
    file-backed matrices.
  - Published two package vignettes covering introductory workflows and
    large-scale analyses with bigmemory.
  - Added an introductory vignette covering the core Cox-PLS workflow.
  - Refreshed the README and website copy to highlight core
    functionality and to demonstrate working examples without warnings,
    including guidance on learning materials and benchmarking resources.
  - Completed package-level documentation with bibliographic references.
  - Updated package metadata to list optional dependencies used in docs
    and benchmarks.

                        Changes in version 0.4.0                        

  - Updated maintainer contact details in DESCRIPTION.
  - Added unit tests for big_pls_cox() and big_pls_cox_gd() stability
    checks.
  - Added unit tests covering the new C++-accelerated Cox PLS
    implementation and cross-validation utilities.

                        Changes in version 0.3.0                        

  - Improved big_pls_cox() numerical stability and added support for
    additional convergence diagnostics in the gradient-descent solver.
  - Refactored stochastic gradient solvers to better integrate with
    bigmemory file-backed matrices.
  - Improved numerical stability of the deviance residual computations.

                        Changes in version 0.2.0                        

  - Expanded documentation examples for deviance residuals and Cox model
    utilities.
  - Added dataset documentation for micro.censure and simulated Cox
    examples.
  - Added pkgdown site configuration and continuous integration
    workflows.

                        Changes in version 0.1.0                        

  - Introduced gPLS and sgPLS model families with support for grouped
    predictors and deviance residual pipelines with cross-validation
    support.

                        Changes in version 0.0.1                        

  - Initial package skeleton with core data objects and helper routines.