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bigPLSR 0.8.0

• Fixed RKHS score prediction to center cross-kernels with the fitted training statistics. A row’s latent score is now invariant to the other rows included in the same prediction batch, including when using the streamed backend.

• Added an optional filematrix row-block provider for future streaming backends. It exposes sequential row/column/block reads as dense R matrices without changing existing bigmemory code paths.

• Added experimental backend = "filematrix" support for NIPALS PLS1/PLS2. The backend uses block-wise file reads and avoids memory-mapped bigmemory access for the predictor/response inputs. Other PLS algorithms remain outside the filematrix backend scope.

bigPLSR 0.7.3

• Big-memory NIPALS PLS1 now uses the same row/chunk streaming backend as PLS2, avoiding the legacy XtX materialization path for very wide predictors.

bigPLSR 0.7.2

• Code and documentation fixes requested by CRAN.

bigPLSR 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.

bigPLSR 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.

bigPLSR 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.

bigPLSR 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.

bigPLSR 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.

bigPLSR 0.6.6

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

bigPLSR 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.

bigPLSR 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.

bigPLSR 0.6.2

• Added cross validation and bootstrap for plsR.

bigPLSR 0.6.1

• Added plots and summaries for pls_fit().

bigPLSR 0.6.0

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

bigPLSR 0.5.0

• Added several plsR implementations. Benchmarks.

bigPLSR 0.4.0

• Maintainer email update
• Added unit tests

bigPLSR 0.3.0

• Code update

bigPLSR 0.2.0

• Improving code and help pages

bigPLSR 0.1.0

• Implementing gpls, sgpls based models

bigPLSR 0.0.1

• Package creation

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