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    "random_subspace_pls",
    "random_subspace_pls_fit",
    "randomization_select",
    "recursive_pls",
    "recursive_pls_fit",
    "rep_select",
    "ridge_pls",
    "ridge_pls_fit",
    "RMSEP",
    "robust_pls",
    "robust_pls_fit",
    "rosa_fit",
    "scars_select",
    "selectivity_ratio_select",
    "selectNcomp",
    "shaving_select",
    "sipls_select",
    "so_pls_fit",
    "spa_select",
    "sparse_pls",
    "sparse_pls_da_fit",
    "sparse_simpls_fit",
    "st_select",
    "stability_select",
    "t2_select",
    "uve_select",
    "vip_select",
    "vip_spa_select",
    "vissa_select",
    "weighted_pls",
    "weighted_pls_fit",
    "wvc_select",
    "wvc_threshold_select"
  ],
  "_help": [
    {
      "page": "aom_pls",
      "title": "AOM-PLS and POP-PLS adaptive operator selection",
      "topics": [
        "aompls",
        "aom_pls",
        "poppls",
        "pop_pls"
      ]
    },
    {
      "page": "aom_preprocess",
      "title": "Adaptive Operator-Mixture preprocessing fit/transform.",
      "topics": [
        "aom_preprocess"
      ]
    },
    {
      "page": "approximate_press",
      "title": "Approximate-PRESS component selection.",
      "topics": [
        "approximate_press"
      ]
    },
    {
      "page": "bagging_pls",
      "title": "Bagging PLS — formula entry point.",
      "topics": [
        "bagging_pls"
      ]
    },
    {
      "page": "bagging_pls_fit",
      "title": "Bagging PLS (bootstrap aggregation of PLS regressors).",
      "topics": [
        "bagging_pls_fit"
      ]
    },
    {
      "page": "bipls_select",
      "title": "biPLS — backward interval PLS.",
      "topics": [
        "bipls_select"
      ]
    },
    {
      "page": "boosting_pls",
      "title": "Boosting PLS — formula entry point.",
      "topics": [
        "boosting_pls"
      ]
    },
    {
      "page": "boosting_pls_fit",
      "title": "Boosting PLS (stage-wise refit with learning_rate).",
      "topics": [
        "boosting_pls_fit"
      ]
    },
    {
      "page": "bve_select",
      "title": "BVE-PLS.",
      "topics": [
        "bve_select"
      ]
    },
    {
      "page": "cars_select",
      "title": "CARS — Competitive Adaptive Reweighted Sampling.",
      "topics": [
        "cars_select"
      ]
    },
    {
      "page": "coef.n4m_fit",
      "title": "Extract the regression coefficients of a [pls()]-fitted model.",
      "topics": [
        "coef.n4m_fit"
      ]
    },
    {
      "page": "coefficient_select",
      "title": "Coefficient-magnitude ranker.",
      "topics": [
        "coefficient_select"
      ]
    },
    {
      "page": "continuum_regression",
      "title": "Continuum regression — formula entry point.",
      "topics": [
        "continuum_regression"
      ]
    },
    {
      "page": "continuum_regression_fit",
      "title": "Continuum regression (tau in [0, 1]).",
      "topics": [
        "continuum_regression_fit"
      ]
    },
    {
      "page": "cppls",
      "title": "Canonical Powered PLS — formula entry point.",
      "topics": [
        "cppls"
      ]
    },
    {
      "page": "cppls_fit",
      "title": "Canonical Powered PLS fit (Indahl 2005).",
      "topics": [
        "cppls_fit"
      ]
    },
    {
      "page": "di_pls",
      "title": "Domain-invariant PLS - formula entry point.",
      "topics": [
        "di_pls"
      ]
    },
    {
      "page": "di_pls_fit",
      "title": "Domain-Invariant PLS (Nikzad-Langerodi 2018).",
      "topics": [
        "di_pls_fit"
      ]
    },
    {
      "page": "ds_fit",
      "title": "Direct Standardization (calibration transfer).",
      "topics": [
        "ds_fit"
      ]
    },
    {
      "page": "ecr",
      "title": "Elastic Component Regression — formula entry point.",
      "topics": [
        "ecr"
      ]
    },
    {
      "page": "ecr_fit",
      "title": "Elastic Component Regression (Liu 2009/2010).",
      "topics": [
        "ecr_fit"
      ]
    },
    {
      "page": "emcuve_select",
      "title": "EMCUVE — ensemble Monte Carlo UVE.",
      "topics": [
        "emcuve_select"
      ]
    },
    {
      "page": "fused_sparse_pls_fit",
      "title": "Fused-sparse PLS (L1 + adjacent-coef smoothing).",
      "topics": [
        "fused_sparse_pls_fit"
      ]
    },
    {
      "page": "ga_select",
      "title": "GA-PLS — genetic algorithm variable selection.",
      "topics": [
        "ga_select"
      ]
    },
    {
      "page": "gpr_pls_fit",
      "title": "Gaussian Process Regression on PLS scores (single-target Y).",
      "topics": [
        "gpr_pls_fit"
      ]
    },
    {
      "page": "group_sparse_pls_fit",
      "title": "Group-sparse PLS (group L1 across feature groups).",
      "topics": [
        "group_sparse_pls_fit"
      ]
    },
    {
      "page": "interval_select",
      "title": "Interval selector (iPLS).",
      "topics": [
        "interval_select"
      ]
    },
    {
      "page": "ipw_select",
      "title": "IPW-PLS.",
      "topics": [
        "ipw_select"
      ]
    },
    {
      "page": "irf_select",
      "title": "IRF — Interval Random Frog.",
      "topics": [
        "irf_select"
      ]
    },
    {
      "page": "iriv_select",
      "title": "IRIV — Iteratively Retains Informative Variables.",
      "topics": [
        "iriv_select"
      ]
    },
    {
      "page": "kernel_pls_fit",
      "title": "Non-linear kernel PLS (Rosipal & Trejo 2001).",
      "topics": [
        "kernel_pls_fit"
      ]
    },
    {
      "page": "lw_pls_fit",
      "title": "Locally-weighted PLS (Næs & Centner 1998).",
      "topics": [
        "lw_pls_fit"
      ]
    },
    {
      "page": "mb_pls",
      "title": "Multi-block PLS — formula entry point.",
      "topics": [
        "mb_pls"
      ]
    },
    {
      "page": "mb_pls_fit",
      "title": "Multi-block PLS (block-weighted SIMPLS).",
      "topics": [
        "mb_pls_fit"
      ]
    },
    {
      "page": "mir_pls",
      "title": "MIR-PLS — formula entry point.",
      "topics": [
        "mir_pls"
      ]
    },
    {
      "page": "mir_pls_fit",
      "title": "MIR-PLS — Multivariate Inverse Regression PLS.",
      "topics": [
        "mir_pls_fit"
      ]
    },
    {
      "page": "missing_aware_nipals",
      "title": "Missing-aware NIPALS — formula entry point.",
      "topics": [
        "missing_aware_nipals"
      ]
    },
    {
      "page": "missing_aware_nipals_fit",
      "title": "Missing-aware NIPALS PLS (Nelson 1996).",
      "topics": [
        "missing_aware_nipals_fit"
      ]
    },
    {
      "page": "n_pls_fit",
      "title": "N-PLS (3-way tensor) regression. `X_flat` is the flattened (n, mode_j*mode_k) matrix.",
      "topics": [
        "n_pls_fit"
      ]
    },
    {
      "page": "pls4all_abi_version",
      "title": "Loaded ABI version as an integer vector (major, minor, patch).",
      "topics": [
        "n4m_abi_version"
      ]
    },
    {
      "page": "pls4all_fit",
      "title": "Fit a PLS regression model via the libn4m C ABI.",
      "topics": [
        "n4m_fit"
      ]
    },
    {
      "page": "n4m_method",
      "title": "Low-level n4m method dispatcher.",
      "topics": [
        "n4m_method",
        "pls4all_method"
      ]
    },
    {
      "page": "pls4all_predict",
      "title": "Predict with a fitted n4m model.",
      "topics": [
        "n4m_predict"
      ]
    },
    {
      "page": "pls4all_version",
      "title": "Runtime version string of the loaded libn4m.",
      "topics": [
        "n4m_version"
      ]
    },
    {
      "page": "o2pls",
      "title": "O2-PLS — formula entry point (uses n_predictive for component count).",
      "topics": [
        "o2pls"
      ]
    },
    {
      "page": "o2pls_fit",
      "title": "O2-PLS (bi-directional OPLS).",
      "topics": [
        "o2pls_fit"
      ]
    },
    {
      "page": "on_pls_fit",
      "title": "OnPLS — Orthogonal multi-block PLS (joint + unique loadings).",
      "topics": [
        "on_pls_fit"
      ]
    },
    {
      "page": "one_se_rule",
      "title": "One-SE rule from a (max_components × n_folds) fold RMSE matrix.",
      "topics": [
        "one_se_rule"
      ]
    },
    {
      "page": "opls",
      "title": "Formula-based OPLS regression wrapper around the n4m C ABI.",
      "topics": [
        "opls"
      ]
    },
    {
      "page": "pds_fit",
      "title": "Piecewise Direct Standardization (calibration transfer).",
      "topics": [
        "pds_fit"
      ]
    },
    {
      "page": "pls",
      "title": "Formula-based PLS regression wrapper around the n4m C ABI",
      "topics": [
        "pls"
      ]
    },
    {
      "page": "pls_cox_fit",
      "title": "PLS-Cox proportional hazards.",
      "topics": [
        "pls_cox_fit"
      ]
    },
    {
      "page": "pls_diagnostics",
      "title": "PLS diagnostics: T², Q, DModX from a fitted model.",
      "topics": [
        "pls_diagnostics"
      ]
    },
    {
      "page": "pls_glm",
      "title": "PLS-GLM — formula entry point. Default is Gaussian; set `family = \"poisson\"` for Poisson IRLS.",
      "topics": [
        "pls_glm"
      ]
    },
    {
      "page": "pls_glm_fit",
      "title": "PLS-GLM — Gaussian (default) or Poisson IRLS.",
      "topics": [
        "pls_glm_fit"
      ]
    },
    {
      "page": "pls_lda_fit",
      "title": "PLS-LDA — Linear Discriminant Analysis on PLS scores.",
      "topics": [
        "pls_lda_fit"
      ]
    },
    {
      "page": "pls_logistic_fit",
      "title": "Multinomial logistic regression on PLS scores.",
      "topics": [
        "pls_logistic_fit"
      ]
    },
    {
      "page": "pls_mdatools",
      "title": "mdatools-style matrix PLS interface",
      "topics": [
        "pls_mdatools",
        "predict.n4m_mdatools_pls"
      ]
    },
    {
      "page": "pls_monitoring",
      "title": "PLS process monitoring (Hotelling T² + Q with alarms).",
      "topics": [
        "pls_monitoring"
      ]
    },
    {
      "page": "pls_qda_fit",
      "title": "PLS-QDA (Quadratic Discriminant Analysis on PLS scores).",
      "topics": [
        "pls_qda_fit"
      ]
    },
    {
      "page": "plsr",
      "title": "PLS package compatibility facade",
      "topics": [
        "coef.n4m_mvr",
        "MSEP",
        "mvr",
        "pcr",
        "plsr",
        "predict.n4m_mvr",
        "R2",
        "RMSEP",
        "selectNcomp"
      ]
    },
    {
      "page": "predict.n4m_fit",
      "title": "Predict from a [pls()]-fitted model.",
      "topics": [
        "predict.n4m_fit"
      ]
    },
    {
      "page": "predict.n4m_method_fit",
      "title": "Predict from a MethodResult-based n4m fit.",
      "topics": [
        "predict.n4m_method_fit"
      ]
    },
    {
      "page": "pso_select",
      "title": "PSO-PLS (Binary Particle Swarm Optimization).",
      "topics": [
        "pso_select"
      ]
    },
    {
      "page": "random_frog_select",
      "title": "Random Frog (Phase 5g).",
      "topics": [
        "random_frog_select"
      ]
    },
    {
      "page": "random_subspace_pls",
      "title": "Random-subspace PLS — formula entry point.",
      "topics": [
        "random_subspace_pls"
      ]
    },
    {
      "page": "random_subspace_pls_fit",
      "title": "Random-subspace PLS (Ho 1998).",
      "topics": [
        "random_subspace_pls_fit"
      ]
    },
    {
      "page": "randomization_select",
      "title": "Randomization test selector.",
      "topics": [
        "randomization_select"
      ]
    },
    {
      "page": "recursive_pls",
      "title": "Recursive PLS - formula entry point.",
      "topics": [
        "recursive_pls"
      ]
    },
    {
      "page": "recursive_pls_fit",
      "title": "Moving-window recursive PLS.",
      "topics": [
        "recursive_pls_fit"
      ]
    },
    {
      "page": "rep_select",
      "title": "REP-PLS.",
      "topics": [
        "rep_select"
      ]
    },
    {
      "page": "ridge_pls",
      "title": "Ridge PLS — formula entry point.",
      "topics": [
        "ridge_pls"
      ]
    },
    {
      "page": "ridge_pls_fit",
      "title": "L2-augmented PLS regression.",
      "topics": [
        "ridge_pls_fit"
      ]
    },
    {
      "page": "robust_pls",
      "title": "Robust PLS — formula entry point.",
      "topics": [
        "robust_pls"
      ]
    },
    {
      "page": "robust_pls_fit",
      "title": "Robust PLS via Huber IRLS.",
      "topics": [
        "robust_pls_fit"
      ]
    },
    {
      "page": "rosa_fit",
      "title": "ROSA — Response-Oriented Sequential Alternation.",
      "topics": [
        "rosa_fit"
      ]
    },
    {
      "page": "scars_select",
      "title": "SCARS — Stability + CARS.",
      "topics": [
        "scars_select"
      ]
    },
    {
      "page": "selectivity_ratio_select",
      "title": "Selectivity-ratio ranker.",
      "topics": [
        "selectivity_ratio_select"
      ]
    },
    {
      "page": "shaving_select",
      "title": "Shaving selector.",
      "topics": [
        "shaving_select"
      ]
    },
    {
      "page": "sipls_select",
      "title": "siPLS — synergistic interval PLS.",
      "topics": [
        "sipls_select"
      ]
    },
    {
      "page": "so_pls_fit",
      "title": "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.",
      "topics": [
        "so_pls_fit"
      ]
    },
    {
      "page": "spa_select",
      "title": "SPA — Successive Projections Algorithm.",
      "topics": [
        "spa_select"
      ]
    },
    {
      "page": "sparse_pls",
      "title": "Sparse SIMPLS — formula entry point.",
      "topics": [
        "sparse_pls"
      ]
    },
    {
      "page": "sparse_pls_da_fit",
      "title": "Sparse PLS-DA classifier (`y_labels` is an integer vector of class IDs).",
      "topics": [
        "sparse_pls_da_fit"
      ]
    },
    {
      "page": "sparse_simpls_fit",
      "title": "Sparse SIMPLS fit.",
      "topics": [
        "sparse_simpls_fit"
      ]
    },
    {
      "page": "st_select",
      "title": "ST-PLS — score-threshold selector.",
      "topics": [
        "st_select"
      ]
    },
    {
      "page": "stability_select",
      "title": "Stability selector (coefficient stability, MCUVE-style).",
      "topics": [
        "stability_select"
      ]
    },
    {
      "page": "t2_select",
      "title": "T2-PLS - sweep over alpha thresholds.",
      "topics": [
        "t2_select"
      ]
    },
    {
      "page": "uve_select",
      "title": "UVE — Uninformative Variable Elimination.",
      "topics": [
        "uve_select"
      ]
    },
    {
      "page": "vip_select",
      "title": "VIP (Variable Importance in Projection) ranker.",
      "topics": [
        "vip_select"
      ]
    },
    {
      "page": "vip_spa_select",
      "title": "VIP-SPA hybrid selector.",
      "topics": [
        "vip_spa_select"
      ]
    },
    {
      "page": "vissa_select",
      "title": "VISSA — Variable Iterative Space Shrinkage Approach.",
      "topics": [
        "vissa_select"
      ]
    },
    {
      "page": "weighted_pls",
      "title": "Sample-weighted PLS — formula entry point.",
      "topics": [
        "weighted_pls"
      ]
    },
    {
      "page": "weighted_pls_fit",
      "title": "Sample-weighted PLS (sqrt(w)-prescaled SIMPLS).",
      "topics": [
        "weighted_pls_fit"
      ]
    },
    {
      "page": "wvc_select",
      "title": "WVC-PLS — weighted vector correlation top-k selector.",
      "topics": [
        "wvc_select"
      ]
    },
    {
      "page": "wvc_threshold_select",
      "title": "WVC-threshold selector.",
      "topics": [
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      ]
    }
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      "headings": [
        "Quick start (matrix API)",
        "Formula interface",
        "Version and ABI",
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      "created": "2026-05-19 06:29:24",
      "modified": "2026-05-28 07:12:22",
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