Changes in version 0.7.2 (2025-12-01)                  

  - Code and documentation fixes requested by CRAN.

                        Changes in version 0.7.1                        

  - New tuning option: options(bigPLSR.stream.block_align = 8192L). All
    streamed backends (bigmem SIMPLS, streamed scores, RKHS/klogitpls
    Gram passes, and bigmem predict) round their chunk_size up to a
    multiple of this alignment, then clamp to the available number of
    rows. Typical sweet spots are 4096–16384 on modern CPUs.
  - If you always need scores on disk, prefer scores = "big" to avoid
    large R dense allocations; it streams directly into a big.matrix.
  - Added benchmarks results and analysis as two vignettes.

                        Changes in version 0.7.0                        

  - Added plot_pls_bootstrap_scores() and group-aware ellipses for
    plot_pls_biplot() to visualise latent structures.
  - Exposed bigPLSR_stream_kstats() for streamed RKHS centering
    statistics and corrected the bigmemory RKHS interface to accept
    dense response blocks.

                        Changes in version 0.6.9                        

  - Stabilised kernel logistic PLS class weighting, reinstated IRLS
    fallbacks and improved dense/big-memory parity.
  - Reworked the Kalman-filter state helper to reuse the SIMPLS backend,
    ensuring identical coefficients/intercepts to batch fits.
  - Added dedicated RKHS/RKHS-XY and plotting vignettes, and refreshed
    the PLS1/PLS2 benchmarking guides with notes on the new algorithms
    and parallel helpers.

                        Changes in version 0.6.8                        

  - Added optional future-powered parallel execution to
    pls_cross_validate() and pls_bootstrap().
  - Extended pls_bootstrap() with (X, Y) and (X, T) strategies,
    percentile and BCa confidence intervals, numerical summaries, and
    coefficient boxplots.
  - Added group-aware score plotting with confidence ellipses in
    plot_pls_individuals().
  - Added vignettes covering cross-validation/information-criteria
    workflows and bootstrap diagnostics.

                        Changes in version 0.6.7                        

  - kernelpls on backend='bigmem' now uses streaming XXᵗ/column paths;
    the previous dense fallback was removed. Control with
    options(bigPLSR.kpls_gram = 'rows'|'cols'|'auto') and
    bigPLSR.chunk_rows, bigPLSR.chunk_cols.

                        Changes in version 0.6.6                        

  - Vignettes: Kernel and Streaming PLS Methods, Automatic Algorithm
    Selection.
  - Stub C++ entry points for RKHS / kernel logistic / sparse KPLS /
    KF-PLS.

                        Changes in version 0.6.5                        

  - Algorithm auto-selection: new internal heuristic chooses among
      - XtX SIMPLS (standard cross-product SIMPLS),
      - XXt ("widekernelpls") for n << p,
      - NIPALS when memory is tight or rank is low. Tuned by
        options(bigPLSR.mem_budget_gb = 8). Users can override with
        algorithm=.
  - Kernel-style PLS routes: algorithm = "kernelpls" and algorithm =
    "widekernelpls" implementing Dayal & MacGregor–style (1997) kernel
    PLS in X-space and wide-X (XXᵗ) space.
  - Implemented high-performance kernel and wide-kernel PLS algorithms
    in pls_fit() for both dense and bigmemory backends using
    RcppArmadillo.
  - Introduced optional coefficient thresholding.
  - Added fast-running examples to all exported functions to improve
    documentation usability on CRAN.

                        Changes in version 0.6.4                        

  - Added kernel PLS and wide-kernel PLS algorithms to pls_fit() for
    both dense and bigmemory backends.
  - Refreshed plotting helpers with variable plots, arrow-based loadings
    and a dedicated VIP bar plot.
  - Introduced convenience prediction wrappers, information-criteria
    helpers, and expanded cross-validation/bootstrapping utilities to
    support the new algorithms.
  - Improved summaries with explained-variance reporting and updated
    package documentation.

                        Changes in version 0.6.2                        

  - Added cross validation and bootstrap for plsR.

                        Changes in version 0.6.1                        

  - Added plots and summaries for pls_fit().

                        Changes in version 0.6.0                        

  - Added unified path pls_fit() for plsR regression that features :
    dense and bigmemory, simpls and nipals.

                        Changes in version 0.5.0                        

  - Added several plsR implementations. Benchmarks.

                        Changes in version 0.4.0                        

  - Maintainer email update
  - Added unit tests

                        Changes in version 0.3.0                        

  - Code update

                        Changes in version 0.2.0                        

  - Improving code and help pages

                        Changes in version 0.1.0                        

  - Implementing gpls, sgpls based models

                        Changes in version 0.0.1                        

  - Package creation