Package: pls4all 1.3.6

Gregory Beurier

pls4all: Partial Least Squares and Chemometrics Engine

A portable Partial Least Squares engine for chemometrics: the slim, PLS-focused distribution carved from the 'nirs4all-methods' library. It ships every method built on the shared PLS core (NIPALS, SIMPLS, SVD, kernel, wide-kernel, orthogonal-scores, power, randomized SVD, PCR): regression variants (sparse SIMPLS, CPPLS, weighted, robust, ridge, continuum, multi-block, GLM, MIR), adaptive AOM-PLS / POP-PLS operator selection, variable-selection methods (SPA, CARS, GA, random frog, stability selection, VIP), PLS diagnostics (Hotelling T2, Q residuals, DModX), and calibration transfer (PDS, DS). The spectroscopy-specific surface (spectral preprocessing, augmentation, sample filters, signal-type detection) lives in the full 'nirs4all-methods' distribution. The same 'C++17' numerical core powers both; here it is vendored and compiled from source at install time, with no external system libraries required.

Authors:Gregory Beurier [aut, cre], pls4all contributors [ctb]

pls4all_1.3.6.tar.gz
pls4all_1.3.6.zip(r-4.7-x86_64)pls4all_1.3.6.zip(r-4.7-arm64)pls4all_1.3.6.zip(r-4.6-x86_64)pls4all_1.3.6.zip(r-4.6-arm64)pls4all_1.3.6.zip(r-4.5-x86_64)
pls4all_1.3.6.tgz(r-4.6-x86_64)pls4all_1.3.6.tgz(r-4.6-arm64)pls4all_1.3.6.tgz(r-4.5-x86_64)pls4all_1.3.6.tgz(r-4.5-arm64)
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pls4all_1.3.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
pls4all/json (API)

# Install 'pls4all' in R:

install.packages('pls4all', repos = c('https://gbeurier.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/gbeurier/nirs4all-methods/issues

Uses libs:

c++– GNU Standard C++ Library v3

On CRAN:

cpp

5.27 score 22 stars 9 scripts 131 exports 0 dependencies

Last updated from:131a15a6bb. Checks:15 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	246
linux-devel-x86_64	OK	265
source / vignettes	OK	507
linux-release-arm64	OK	245
linux-release-x86_64	OK	267
macos-release-arm64	OK	270
macos-release-x86_64	OK	715
macos-oldrel-arm64	OK	247
macos-oldrel-x86_64	OK	436
windows-devel-arm64	OK	503
windows-devel-x86_64	OK	433
windows-release-arm64	OK	515
windows-release-x86_64	OK	434
windows-oldrel-x86_64	OK	379
wasm-release	OK	249

Dependencies:

Introduction to pls4all

Quick start (matrix API) | Formula interface | Version and ABI | Supported algorithms | Further reading

Rendered frompls4all.Rmdusingknitr::knitr

Last update: 2026-05-28
Started: 2026-05-19

Help page	Topics
AOM-PLS and POP-PLS adaptive operator selection	aompls aom_pls poppls pop_pls
Adaptive Operator-Mixture preprocessing fit/transform.	aom_preprocess
Approximate-PRESS component selection.	approximate_press
Bagging PLS — formula entry point.	bagging_pls
Bagging PLS (bootstrap aggregation of PLS regressors).	bagging_pls_fit
biPLS — backward interval PLS.	bipls_select n4m_feature_select_bipls
Boosting PLS — formula entry point.	boosting_pls
Boosting PLS (stage-wise refit with learning_rate).	boosting_pls_fit
BVE-PLS.	bve_select n4m_feature_select_bve
CARS — Competitive Adaptive Reweighted Sampling.	cars_select n4m_feature_select_cars
Extract the regression coefficients of a [pls()]-fitted model.	coef.n4m_fit
Coefficient-magnitude ranker.	coefficient_select n4m_feature_select_coefficient
Continuum regression — formula entry point.	continuum_regression
Continuum regression (tau in [0, 1]).	continuum_regression_fit
Canonical Powered PLS — formula entry point.	cppls
Canonical Powered PLS fit (Indahl 2005).	cppls_fit
Domain-invariant PLS - formula entry point.	di_pls
Domain-Invariant PLS (Nikzad-Langerodi 2018).	di_pls_fit
Direct Standardization (calibration transfer).	ds_fit
Elastic Component Regression — formula entry point.	ecr
Elastic Component Regression (Liu 2009/2010).	ecr_fit
EMCUVE — ensemble Monte Carlo UVE.	emcuve_select n4m_feature_select_emcuve
Fused-sparse PLS (L1 + adjacent-coef smoothing).	fused_sparse_pls_fit
GA-PLS — genetic algorithm variable selection.	ga_select n4m_feature_select_ga
Gaussian Process Regression on PLS scores (single-target Y).	gpr_pls_fit
Group-sparse PLS (group L1 across feature groups).	group_sparse_pls_fit
Interval selector (iPLS).	interval_select n4m_feature_select_interval
IPW-PLS.	ipw_select n4m_feature_select_ipw
IRF — Interval Random Frog.	irf_select n4m_feature_select_irf
IRIV — Iteratively Retains Informative Variables.	iriv_select n4m_feature_select_iriv
Non-linear kernel PLS (Rosipal & Trejo 2001).	kernel_pls_fit
Locally-weighted PLS (Næs & Centner 1998).	lw_pls_fit
Multi-block PLS — formula entry point.	mb_pls
Multi-block PLS (block-weighted SIMPLS).	mb_pls_fit
MIR-PLS — formula entry point.	mir_pls
MIR-PLS — Multivariate Inverse Regression PLS.	mir_pls_fit
Missing-aware NIPALS — formula entry point.	missing_aware_nipals
Missing-aware NIPALS PLS (Nelson 1996).	missing_aware_nipals_fit
N-PLS (3-way tensor) regression. `X_flat` is the flattened (n, mode_j*mode_k) matrix.	n_pls_fit
Loaded ABI version as an integer vector (major, minor, patch).	n4m_abi_version
Fit a PLS regression model via the libn4m C ABI.	n4m_fit
Low-level n4m method dispatcher.	n4m_method pls4all_method
Predict with a fitted n4m model.	n4m_predict
Runtime version string of the loaded libn4m.	n4m_version
O2-PLS — formula entry point (uses n_predictive for component count).	o2pls
O2-PLS (bi-directional OPLS).	o2pls_fit
OnPLS — Orthogonal multi-block PLS (joint + unique loadings).	on_pls_fit
One-SE rule from a (max_components × n_folds) fold RMSE matrix.	one_se_rule
Formula-based OPLS regression wrapper around the n4m C ABI.	opls
Piecewise Direct Standardization (calibration transfer).	pds_fit
Formula-based PLS regression wrapper around the n4m C ABI	pls
PLS-Cox proportional hazards.	pls_cox_fit
PLS diagnostics: T², Q, DModX from a fitted model.	pls_diagnostics
PLS-GLM — formula entry point. Default is Gaussian; set `family = "poisson"` for Poisson IRLS.	pls_glm
PLS-GLM — Gaussian (default) or Poisson IRLS.	pls_glm_fit
PLS-LDA — Linear Discriminant Analysis on PLS scores.	pls_lda_fit
Multinomial logistic regression on PLS scores.	pls_logistic_fit
mdatools-style matrix PLS interface	pls_mdatools predict.n4m_mdatools_pls
PLS process monitoring (Hotelling T² + Q with alarms).	pls_monitoring
PLS-QDA (Quadratic Discriminant Analysis on PLS scores).	pls_qda_fit
PLS package compatibility facade	coef.n4m_mvr MSEP mvr pcr plsr predict.n4m_mvr R2 RMSEP selectNcomp
Predict from a [pls()]-fitted model.	predict.n4m_fit
Predict from a MethodResult-based n4m fit.	predict.n4m_method_fit
PSO-PLS (Binary Particle Swarm Optimization).	n4m_feature_select_pso pso_select
Random Frog (Phase 5g).	n4m_feature_select_random_frog random_frog_select
Random-subspace PLS — formula entry point.	random_subspace_pls
Random-subspace PLS (Ho 1998).	random_subspace_pls_fit
Randomization test selector.	n4m_feature_select_randomization randomization_select
Recursive PLS - formula entry point.	recursive_pls
Moving-window recursive PLS.	recursive_pls_fit
REP-PLS.	n4m_feature_select_rep rep_select
Ridge PLS — formula entry point.	ridge_pls
L2-augmented PLS regression.	ridge_pls_fit
Robust PLS — formula entry point.	robust_pls
Robust PLS via Huber IRLS.	robust_pls_fit
ROSA — Response-Oriented Sequential Alternation.	rosa_fit
SCARS — Stability + CARS.	n4m_feature_select_scars scars_select
Selectivity-ratio ranker.	n4m_feature_select_selectivity_ratio selectivity_ratio_select
Shaving selector.	n4m_feature_select_shaving shaving_select
siPLS — synergistic interval PLS.	n4m_feature_select_sipls sipls_select
Sequential & Orthogonalised multi-block PLS (Næs et al. 2011). `block_sizes` integer vector summing to ncol(X); `n_components_per_block` integer vector of same length.	so_pls_fit
SPA — Successive Projections Algorithm.	n4m_feature_select_spa spa_select
Sparse SIMPLS — formula entry point.	sparse_pls
Sparse PLS-DA classifier (`y_labels` is an integer vector of class IDs).	sparse_pls_da_fit
Sparse SIMPLS fit.	sparse_simpls_fit
ST-PLS — score-threshold selector.	n4m_feature_select_st st_select
Stability selector (coefficient stability, MCUVE-style).	n4m_feature_select_stability stability_select
T2-PLS - sweep over alpha thresholds.	n4m_feature_select_t2 t2_select
UVE — Uninformative Variable Elimination.	n4m_feature_select_uve uve_select
VIP (Variable Importance in Projection) ranker.	n4m_feature_select_vip vip_select
VIP-SPA hybrid selector.	n4m_feature_select_vip_spa vip_spa_select
VISSA — Variable Iterative Space Shrinkage Approach.	n4m_feature_select_vissa vissa_select
Sample-weighted PLS — formula entry point.	weighted_pls
Sample-weighted PLS (sqrt(w)-prescaled SIMPLS).	weighted_pls_fit
WVC-PLS — weighted vector correlation top-k selector.	n4m_feature_select_wvc wvc_select
WVC-threshold selector.	n4m_feature_select_wvc_threshold wvc_threshold_select

Package: pls4all 1.3.6

pls4all: Partial Least Squares and Chemometrics Engine

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