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  "Title": "Partial Least Squares and Chemometrics Engine",
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  "Authors@R": "c(person(\"Gregory\", \"Beurier\", role = c(\"aut\", \"cre\"),\nemail = \"gregory.beurier@cirad.fr\"),\nperson(\"pls4all\", \"contributors\", role = \"ctb\"))",
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  "License": "CeCILL (== 2.1)",
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  "BugReports": "https://github.com/GBeurier/nirs4all-methods/issues",
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  "Repository": "https://gbeurier.r-universe.dev",
  "Date/Publication": "2026-10-13 09:45:01 UTC",
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    "message": "fix(bindings): align Rust and R ABI assertions with ABI 2.19\n\nThe Rust binding constants moved to ABI 2.19, but the Rust tests still\nexpected a 2.17.0 manifest and writer header. The Rust binding workflow has\nfailed on main since the 2.18 bump. The R version test pinned the same stale\nminor and would have failed at release-r time.\n\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n",
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  "_stars": 22,
  "_contributors": [
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      "count": 936,
      "uuid": 95907737
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  "_userbio": {
    "uuid": 95907737,
    "type": "user",
    "name": "Gregory Beurier",
    "followers": 11,
    "description": "Researcher at CIRAD.\r\nInterested in complex systems modeling and AI (machine learning, deep learning, multiagent systems, optimization)."
  },
  "_downloads": {
    "count": 0,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/pls4all"
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  "_devurl": "https://github.com/gbeurier/nirs4all-methods",
  "_searchresults": 9,
  "_topics": [
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  ],
  "_rbuild": "4.6.1",
  "_assets": [
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    "extra/citation.json",
    "extra/citation.txt",
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    "extra/manual.pdf",
    "extra/pls4all.html",
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  "_cranurl": false,
  "_exports": [
    "aom_pls",
    "aom_preprocess",
    "aompls",
    "approximate_press",
    "bagging_pls",
    "bagging_pls_fit",
    "bipls_select",
    "boosting_pls",
    "boosting_pls_fit",
    "bve_select",
    "cars_select",
    "coefficient_select",
    "continuum_regression",
    "continuum_regression_fit",
    "cppls",
    "cppls_fit",
    "di_pls",
    "di_pls_fit",
    "ds_fit",
    "ecr",
    "ecr_fit",
    "emcuve_select",
    "fused_sparse_pls_fit",
    "ga_select",
    "gpr_pls_fit",
    "group_sparse_pls_fit",
    "interval_select",
    "ipw_select",
    "irf_select",
    "iriv_select",
    "kernel_pls_fit",
    "lw_pls_fit",
    "mb_pls",
    "mb_pls_fit",
    "mir_pls",
    "mir_pls_fit",
    "missing_aware_nipals",
    "missing_aware_nipals_fit",
    "MSEP",
    "mvr",
    "n_pls_fit",
    "n4m_abi_version",
    "n4m_feature_select_bipls",
    "n4m_feature_select_bve",
    "n4m_feature_select_cars",
    "n4m_feature_select_coefficient",
    "n4m_feature_select_emcuve",
    "n4m_feature_select_ga",
    "n4m_feature_select_interval",
    "n4m_feature_select_ipw",
    "n4m_feature_select_irf",
    "n4m_feature_select_iriv",
    "n4m_feature_select_pso",
    "n4m_feature_select_random_frog",
    "n4m_feature_select_randomization",
    "n4m_feature_select_rep",
    "n4m_feature_select_scars",
    "n4m_feature_select_selectivity_ratio",
    "n4m_feature_select_shaving",
    "n4m_feature_select_sipls",
    "n4m_feature_select_spa",
    "n4m_feature_select_st",
    "n4m_feature_select_stability",
    "n4m_feature_select_t2",
    "n4m_feature_select_uve",
    "n4m_feature_select_vip",
    "n4m_feature_select_vip_spa",
    "n4m_feature_select_vissa",
    "n4m_feature_select_wvc",
    "n4m_feature_select_wvc_threshold",
    "n4m_fit",
    "n4m_method",
    "n4m_predict",
    "n4m_version",
    "o2pls",
    "o2pls_fit",
    "on_pls_fit",
    "one_se_rule",
    "opls",
    "pcr",
    "pds_fit",
    "pls",
    "pls_cox_fit",
    "pls_diagnostics",
    "pls_glm",
    "pls_glm_fit",
    "pls_lda_fit",
    "pls_logistic_fit",
    "pls_mdatools",
    "pls_monitoring",
    "pls_qda_fit",
    "pls4all_method",
    "plsr",
    "pop_pls",
    "poppls",
    "pso_select",
    "R2",
    "random_frog_select",
    "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",
        "n4m_feature_select_bipls"
      ]
    },
    {
      "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",
        "n4m_feature_select_bve"
      ]
    },
    {
      "page": "cars_select",
      "title": "CARS — Competitive Adaptive Reweighted Sampling.",
      "topics": [
        "cars_select",
        "n4m_feature_select_cars"
      ]
    },
    {
      "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",
        "n4m_feature_select_coefficient"
      ]
    },
    {
      "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",
        "n4m_feature_select_emcuve"
      ]
    },
    {
      "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",
        "n4m_feature_select_ga"
      ]
    },
    {
      "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",
        "n4m_feature_select_interval"
      ]
    },
    {
      "page": "ipw_select",
      "title": "IPW-PLS.",
      "topics": [
        "ipw_select",
        "n4m_feature_select_ipw"
      ]
    },
    {
      "page": "irf_select",
      "title": "IRF — Interval Random Frog.",
      "topics": [
        "irf_select",
        "n4m_feature_select_irf"
      ]
    },
    {
      "page": "iriv_select",
      "title": "IRIV — Iteratively Retains Informative Variables.",
      "topics": [
        "iriv_select",
        "n4m_feature_select_iriv"
      ]
    },
    {
      "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": [
        "n4m_feature_select_pso",
        "pso_select"
      ]
    },
    {
      "page": "random_frog_select",
      "title": "Random Frog (Phase 5g).",
      "topics": [
        "n4m_feature_select_random_frog",
        "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": [
        "n4m_feature_select_randomization",
        "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": [
        "n4m_feature_select_rep",
        "rep_select"
      ]
    },
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      "page": "ridge_pls",
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