Quick start (matrix API) | Formula interface | Version and ABI | Supported algorithms | Further reading
Package: n4m 1.3.13
Gregory Beurier
n4m: Portable Partial Least Squares and NIRS Engine
Implements a portable Partial Least Squares (PLS) and Near-Infrared Spectroscopy (NIRS) engine. Provides fit/predict wrappers for the shipped PLS regression solvers (NIPALS, SIMPLS, SVD, kernel, wide-kernel, orthogonal-scores, power, randomized SVD, PCR), variants (sparse SIMPLS, CPPLS, weighted, robust, ridge, continuum, multi-block, GLM, MIR), adaptive AOM-PLS and POP-PLS operator selection, variable-selection methods (SPA, CARS, GA, random frog, stability selection, VIP), diagnostics (Hotelling T2, Q residuals, DModX), and calibration transfer (PDS, DS). The 'C++17' implementation is vendored and compiled from source at install time; no external system libraries are required.
Authors:
n4m_1.3.13.tar.gz
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n4m_1.3.13.tgz(r-4.6-x86_64)n4m_1.3.13.tgz(r-4.6-arm64)n4m_1.3.13.tgz(r-4.5-x86_64)n4m_1.3.13.tgz(r-4.5-arm64)
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n4m_1.3.13.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
n4m/json (API)
| # Install 'n4m' in R: |
| install.packages('n4m', repos = c('https://gbeurier.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/gbeurier/nirs4all-methods/issues
Last updated from:fa8ff57603 (on main). Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 286 | ||
| linux-devel-x86_64 | OK | 317 | ||
| source / vignettes | OK | 536 | ||
| linux-release-arm64 | OK | 277 | ||
| linux-release-x86_64 | OK | 351 | ||
| macos-release-arm64 | OK | 259 | ||
| macos-release-x86_64 | OK | 754 | ||
| macos-oldrel-arm64 | OK | 304 | ||
| macos-oldrel-x86_64 | OK | 814 | ||
| windows-devel-arm64 | OK | 600 | ||
| windows-devel-x86_64 | OK | 461 | ||
| windows-release-arm64 | OK | 545 | ||
| windows-release-x86_64 | OK | 434 | ||
| windows-oldrel-x86_64 | OK | 437 | ||
| wasm-release | OK | 264 |
Exports:aom_plsaom_preprocessaomplsapproximate_pressarea_normalization_transformbagging_plsbagging_pls_fitbipls_selectboosting_plsboosting_pls_fitbve_selectcars_selectcoefficient_selectcontinuum_regressioncontinuum_regression_fitcpplscppls_fitdetrend_transformdi_plsdi_pls_fitds_fitecrecr_fitemcuve_selectemsc_fitemsc_transformfused_sparse_pls_fitga_selectgcu_fitgpr_pls_fitgroup_sparse_pls_fitinterval_selectipw_selectirf_selectiriv_selectkennard_stone_splitkernel_pls_fitlocal_snv_transformlvse_fitlw_pls_fitmb_plsmb_pls_fitmir_plsmir_pls_fitmissing_aware_nipalsmissing_aware_nipals_fitmsc_fitmsc_transformMSEPmvrn_pls_fitn4m_abi_versionn4m_affine_fitn4m_affine_model_exportn4m_affine_supported_methodsn4m_airplsn4m_aom_chain_fixed_fitn4m_aom_chain_ridge_plsn4m_aom_chain_sweepn4m_aom_plsn4m_aom_pls_superblockn4m_aom_preprocessingn4m_aom_ridge_pls_superblockn4m_aom_sweepn4m_approximate_pressn4m_area_normalizationn4m_arplsn4m_aslsn4m_augmentn4m_augmentation_applyn4m_bagging_plsn4m_band_maskn4m_band_perturbn4m_baselinen4m_batch_effectn4m_beadsn4m_binned_strat_group_kfoldn4m_biplsn4m_boosting_plsn4m_bven4m_calibrationn4m_carsn4m_channel_dropoutn4m_classesn4m_closen4m_constraintn4m_constructorn4m_contains_training_rowsn4m_continuum_regressionn4m_correlationn4m_cow_alignn4m_cpplsn4m_cropn4m_dead_bandn4m_derivaten4m_detector_rolloffn4m_detrendn4m_di_plsn4m_direct_standardizationn4m_dsn4m_dtw_alignn4m_ecrn4m_edge_artifactsn4m_edge_curvaturen4m_emcuven4m_emscn4m_emsc_distortn4m_epon4m_estimator_exportn4m_estimator_fitn4m_estimator_importn4m_estimator_transformn4m_export_staten4m_fck_staticn4m_feature_select_biplsn4m_feature_select_bven4m_feature_select_carsn4m_feature_select_coefficientn4m_feature_select_emcuven4m_feature_select_gan4m_feature_select_intervaln4m_feature_select_ipwn4m_feature_select_irfn4m_feature_select_irivn4m_feature_select_pson4m_feature_select_random_frogn4m_feature_select_randomizationn4m_feature_select_repn4m_feature_select_scarsn4m_feature_select_selectivity_ration4m_feature_select_shavingn4m_feature_select_siplsn4m_feature_select_span4m_feature_select_stn4m_feature_select_stabilityn4m_feature_select_t2n4m_feature_select_uven4m_feature_select_vipn4m_feature_select_vip_span4m_feature_select_vissan4m_feature_select_wvcn4m_feature_select_wvc_thresholdn4m_first_derivativen4m_fitn4m_flexible_pcan4m_flexible_svdn4m_fraction_to_percentn4m_from_absorbancen4m_fused_sparse_plsn4m_gan4m_gauss_jittern4m_gaussiann4m_gaussian_noisen4m_gpr_plsn4m_group_sparse_plsn4m_haarn4m_hetero_noisen4m_high_leveragen4m_hotelling_t2n4m_iaslsn4m_icoshift_alignn4m_imodpolyn4m_instrument_broadenn4m_intervaln4m_ipwn4m_irfn4m_irivn4m_kbins_discretizern4m_kbins_stratifiedn4m_kennard_stonen4m_kerneln4m_kmeansn4m_kubelka_munkn4m_linear_driftn4m_linear_stack_compressn4m_local_centeringn4m_local_clipn4m_local_densityn4m_local_mixupn4m_local_snvn4m_local_warpn4m_localized_mscn4m_log_transformn4m_lw_plsn4m_magnitude_warpn4m_manifest_jsonn4m_mb_plsn4m_methodn4m_mir_plsn4m_missing_aware_nipalsn4m_mixupn4m_model_descriptorn4m_model_exportn4m_model_importn4m_model_import_linear_predictorn4m_model_inspectn4m_model_pipeline_infon4m_model_selectionn4m_model_selection_kennard_stonen4m_modpolyn4m_moisturen4m_momentsn4m_mscn4m_multimodal_classifiern4m_multimodal_classifier_from_staten4m_multimodal_pipelinen4m_multimodal_pipeline_from_staten4m_multiplicative_noisen4m_normalizen4m_norris_williamsn4m_o2plsn4m_on_plsn4m_operator_pls_stackn4m_optimizern4m_optimizer_askn4m_optimizer_ask_batchn4m_optimizer_bestn4m_optimizer_closen4m_optimizer_enqueuen4m_optimizer_intermediaten4m_optimizer_loadn4m_optimizer_saven4m_optimizer_telln4m_optimizer_trialsn4m_oscn4m_param_categoricaln4m_param_floatn4m_param_intn4m_param_ordinaln4m_param_sorted_tuplen4m_particle_sizen4m_path_lengthn4m_pca_reference_qcn4m_pcrn4m_pdsn4m_percent_to_fractionn4m_piecewise_direct_standardizationn4m_piecewise_mscn4m_piecewise_snvn4m_pls_coxn4m_pls_diagnosticsn4m_pls_fit_simplen4m_pls_glmn4m_pls_ldan4m_pls_logisticn4m_pls_monitoringn4m_pls_qdan4m_pls_regressionn4m_poly_driftn4m_pop_plsn4m_predictn4m_preprocess_capabilitiesn4m_preprocess_exportn4m_preprocess_fitn4m_preprocess_importn4m_preprocess_plann4m_preprocess_stepn4m_preprocess_transformn4m_pson4m_q_residualsn4m_ragged_summaryn4m_random_frogn4m_random_subspace_plsn4m_random_x_opn4m_randomizationn4m_range_discretizern4m_recursiven4m_regression_metricsn4m_regression_ridgen4m_repn4m_resample_transformern4m_resamplern4m_ridgen4m_ridge_active_superblockn4m_ridge_blendern4m_ridge_globaln4m_ridge_mkl_superblockn4m_ridge_plsn4m_ridge_superblockn4m_robust_direct_standardizationn4m_robust_hpon4m_robust_plsn4m_robust_snvn4m_role_pipelinen4m_role_pipeline_exportn4m_role_pipeline_importn4m_role_pipeline_infon4m_role_pipeline_stepsn4m_rolling_balln4m_rosan4m_rotate_translaten4m_runn4m_sample_maskn4m_sapsn4m_savitzky_golayn4m_scarsn4m_scatter_sim_mscn4m_second_derivativen4m_selected_indicesn4m_shavingn4m_signal_type_detectorn4m_simple_scalen4m_siplsn4m_slope_biasn4m_snipn4m_snvn4m_so_plsn4m_span4m_sparse_pls_dan4m_sparse_simplsn4m_spectral_events_qcn4m_spectral_noise_proxyn4m_spectral_qualityn4m_spectral_reference_qcn4m_spike_noisen4m_spline_curve_simplificationn4m_spline_smoothingn4m_spline_x_perturbationsn4m_spline_x_simplificationn4m_spline_y_perturbationsn4m_splitn4m_split_splittern4m_splitter_runn4m_spxyn4m_spxy_foldn4m_spxy_g_foldn4m_stn4m_stabilityn4m_standard_scalen4m_stray_lightn4m_sweepn4m_systematic_circularn4m_t2n4m_temperaturen4m_tensor_plsn4m_to_absorbancen4m_transfer_metricsn4m_transform_savgoln4m_transform_snvn4m_truncated_peakn4m_unsharp_maskn4m_uven4m_variable_selectn4m_variancen4m_versionn4m_vip_span4m_vissan4m_vsnn4m_wavelength_shiftn4m_wavelength_stretchn4m_waveletn4m_wavelet_denoisen4m_wavelet_featuresn4m_wavelet_pcan4m_wavelet_svdn4m_weighted_plsn4m_weighted_snvn4m_wvcn4m_wvc_thresholdn4m_x_outliern4m_xcorr_alignn4m_y_outliero2plso2pls_fiton_pls_fitone_se_ruleoplspcrpds_fitplspls_cox_fitpls_diagnosticspls_glmpls_glm_fitpls_lda_fitpls_logistic_fitpls_mdatoolspls_monitoringpls_qda_fitplsrpop_plspopplspso_selectR2random_frog_selectrandom_subspace_plsrandom_subspace_pls_fitrandomization_selectrecursive_plsrecursive_pls_fitrep_selectridge_fitridge_plsridge_pls_fitRMSEProbust_plsrobust_pls_fitrobust_snv_transformrosa_fitsavgol_transformscars_selectselectivity_ratio_selectselectNcompshaving_selectsipls_selectsnv_transformso_pls_fitspa_selectsparse_plssparse_pls_da_fitsparse_simpls_fitspectral_export_affinespectral_transformst_selectstability_selectt2_selectuve_selectvip_selectvip_spa_selectvissa_selectweighted_plsweighted_pls_fitwvc_selectwvc_threshold_select
Dependencies:
Readme and manuals
Help Manual
| 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 |
| Area normalization transform | area_normalization_transform |
| 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 |
| Polynomial detrend transform | detrend_transform |
| 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 |
| Fit Extended Multiplicative Scatter Correction | emsc_fit |
| Apply fitted Extended Multiplicative Scatter Correction | emsc_transform |
| 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 |
| Kennard-Stone train/test split | kennard_stone_split n4m_model_selection_kennard_stone |
| Non-linear kernel PLS (Rosipal & Trejo 2001). | kernel_pls_fit |
| Local Standard Normal Variate transform | local_snv_transform |
| 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 |
| Fit native Multiplicative Scatter Correction | msc_fit |
| Apply a fitted MSC reference to spectra | msc_transform |
| 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 and predict a native affine MethodResult regressor | n4m_affine_fit predict.n4m_affine_fit |
| Export a fitted affine MethodResult predictor | n4m_affine_model_export |
| List MethodResult regressors marked for native affine conversion | n4m_affine_supported_methods |
| Apply a seeded native X-only training augmentation | n4m_augmentation_apply |
| Constructors of the generic native estimator roles | n4m_airpls n4m_aom_chain_fixed_fit n4m_aom_chain_ridge_pls n4m_aom_chain_sweep n4m_aom_pls n4m_aom_pls_superblock n4m_aom_preprocessing n4m_aom_ridge_pls_superblock n4m_aom_sweep n4m_approximate_press n4m_area_normalization n4m_arpls n4m_asls n4m_bagging_pls n4m_band_mask n4m_band_perturb n4m_baseline n4m_batch_effect n4m_beads n4m_binned_strat_group_kfold n4m_bipls n4m_boosting_pls n4m_bve n4m_calibration n4m_cars n4m_channel_dropout n4m_continuum_regression n4m_correlation n4m_cow_align n4m_cppls n4m_crop n4m_dead_band n4m_derivate n4m_detector_rolloff n4m_detrend n4m_direct_standardization n4m_di_pls n4m_ds n4m_dtw_align n4m_ecr n4m_edge_artifacts n4m_edge_curvature n4m_emcuve n4m_emsc n4m_emsc_distort n4m_epo n4m_estimator_role_constructors n4m_fck_static n4m_first_derivative n4m_flexible_pca n4m_flexible_svd n4m_fraction_to_percent n4m_from_absorbance n4m_fused_sparse_pls n4m_ga n4m_gaussian n4m_gaussian_noise n4m_gauss_jitter n4m_gpr_pls n4m_group_sparse_pls n4m_haar n4m_hetero_noise n4m_high_leverage n4m_hotelling_t2 n4m_iasls n4m_icoshift_align n4m_imodpoly n4m_instrument_broaden n4m_interval n4m_ipw n4m_irf n4m_iriv n4m_kbins_discretizer n4m_kbins_stratified n4m_kennard_stone n4m_kernel n4m_kmeans n4m_kubelka_munk n4m_linear_drift n4m_linear_stack_compress n4m_localized_msc n4m_local_centering n4m_local_clip n4m_local_density n4m_local_mixup n4m_local_snv n4m_local_warp n4m_log_transform n4m_lw_pls n4m_magnitude_warp n4m_mb_pls n4m_mir_pls n4m_missing_aware_nipals n4m_mixup n4m_model_selection n4m_modpoly n4m_moisture n4m_moments n4m_msc n4m_multiplicative_noise n4m_normalize n4m_norris_williams n4m_o2pls n4m_on_pls n4m_operator_pls_stack n4m_osc n4m_particle_size n4m_path_length n4m_pca_reference_qc n4m_pcr n4m_pds n4m_percent_to_fraction n4m_piecewise_direct_standardization n4m_piecewise_msc n4m_piecewise_snv n4m_pls_cox n4m_pls_diagnostics n4m_pls_fit_simple n4m_pls_glm n4m_pls_lda n4m_pls_logistic n4m_pls_monitoring n4m_pls_qda n4m_pls_regression n4m_poly_drift n4m_pop_pls n4m_pso n4m_q_residuals n4m_ragged_summary n4m_randomization n4m_random_frog n4m_random_subspace_pls n4m_random_x_op n4m_range_discretizer n4m_recursive n4m_regression_metrics n4m_rep n4m_resampler n4m_resample_transformer n4m_ridge n4m_ridge_active_superblock n4m_ridge_blender n4m_ridge_global n4m_ridge_mkl_superblock n4m_ridge_pls n4m_ridge_superblock n4m_robust_direct_standardization n4m_robust_hpo n4m_robust_pls n4m_robust_snv n4m_rolling_ball n4m_rosa n4m_rotate_translate n4m_saps n4m_savitzky_golay n4m_scars n4m_scatter_sim_msc n4m_second_derivative n4m_shaving n4m_signal_type_detector n4m_simple_scale n4m_sipls n4m_slope_bias n4m_snip n4m_snv n4m_so_pls n4m_spa n4m_sparse_pls_da n4m_sparse_simpls n4m_spectral_events_qc n4m_spectral_noise_proxy n4m_spectral_quality n4m_spectral_reference_qc n4m_spike_noise n4m_spline_curve_simplification n4m_spline_smoothing n4m_spline_x_perturbations n4m_spline_x_simplification n4m_spline_y_perturbations n4m_split_splitter n4m_spxy n4m_spxy_fold n4m_spxy_g_fold n4m_st n4m_stability n4m_standard_scale n4m_stray_light n4m_sweep n4m_systematic_circular n4m_t2 n4m_temperature n4m_tensor_pls n4m_to_absorbance n4m_transfer_metrics n4m_truncated_peak n4m_unsharp_mask n4m_uve n4m_variable_select n4m_variance n4m_vip_spa n4m_vissa n4m_vsn n4m_wavelength_shift n4m_wavelength_stretch n4m_wavelet n4m_wavelet_denoise n4m_wavelet_features n4m_wavelet_pca n4m_wavelet_svd n4m_weighted_pls n4m_weighted_snv n4m_wvc n4m_wvc_threshold n4m_xcorr_align n4m_x_outlier n4m_y_outlier |
| Generic native estimator roles | n4m_classes n4m_classes.n4m_classifier n4m_contains_training_rows n4m_estimator_export n4m_estimator_fit n4m_estimator_fit.n4m_estimator n4m_estimator_import n4m_estimator_roles n4m_estimator_transform n4m_estimator_transform.n4m_selector n4m_estimator_transform.n4m_transformer n4m_sample_mask n4m_sample_mask.n4m_sample_filter n4m_selected_indices n4m_selected_indices.n4m_selector predict.n4m_classifier predict.n4m_regressor print.n4m_method |
| Fit a PLS regression model via the libn4m C ABI. | n4m_fit |
| Native manifest and method lookup | n4m_constructor n4m_manifest n4m_manifest_json |
| Low-level n4m method dispatcher. | n4m_method |
| Inspect a fully validated N4MM model descriptor | n4m_model_descriptor |
| Export a fitted n4m model as portable N4MM bytes | n4m_model_export |
| Import a fitted model from portable N4MM bytes | n4m_model_import |
| Import an affine predictor as a portable N4MM model | n4m_model_import_linear_predictor |
| Inspect portable N4MM model metadata | n4m_model_inspect |
| Inspect the native preprocessing state embedded in N4MM bytes | n4m_model_pipeline_info |
| Native Raw Multimodal PLS-logistic Classifier | n4m_multimodal_classifier n4m_multimodal_classifier_from_state |
| Complete Native Raw Multimodal Pipeline | n4m_close n4m_close.n4m_multimodal_pipeline n4m_estimator_fit.n4m_multimodal_pipeline n4m_estimator_transform.n4m_multimodal_pipeline n4m_export_state n4m_export_state.n4m_multimodal_pipeline n4m_multimodal_pipeline n4m_multimodal_pipeline_from_state predict.n4m_multimodal_pipeline |
| Native hyperparameter optimization from R | n4m_constraint n4m_optimizer n4m_optimizer_ask n4m_optimizer_ask_batch n4m_optimizer_best n4m_optimizer_close n4m_optimizer_enqueue n4m_optimizer_intermediate n4m_optimizer_load n4m_optimizer_save n4m_optimizer_tell n4m_optimizer_trials n4m_param_categorical n4m_param_float n4m_param_int n4m_param_ordinal n4m_param_sorted_tuple |
| Predict with a fitted n4m model. | n4m_predict |
| Generic native preprocessing pipeline | n4m_preprocess_capabilities n4m_preprocess_export n4m_preprocess_fit n4m_preprocess_import n4m_preprocess_plan n4m_preprocess_step n4m_preprocess_transform |
| Generic native procedures | n4m_augment n4m_augment.n4m_augmenter n4m_procedures n4m_run n4m_run.n4m_procedure n4m_split n4m_split.n4m_splitter |
| Native role pipelines | n4m_classes.n4m_role_pipeline n4m_estimator_fit.n4m_role_pipeline n4m_estimator_transform.n4m_role_pipeline n4m_role_pipeline n4m_role_pipeline_export n4m_role_pipeline_import n4m_role_pipeline_info n4m_role_pipeline_steps predict.n4m_role_pipeline print.n4m_role_pipeline |
| Run one of nine native sample splitters | n4m_splitter_run |
| 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 |
| Direct (closed-form) multi-output Ridge regression. | n4m_regression_ridge ridge_fit |
| 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 |
| Robust Standard Normal Variate transform | robust_snv_transform |
| 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 |
| Portable preprocessing transforms | n4m_transform_savgol n4m_transform_snv savgol_transform snv_transform |
| 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 |
| Native fitted spectral representations | gcu_fit lvse_fit spectral_export_affine spectral_transform |
| 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 |