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_sizeup 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.