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    "message": "test(python): pin the PCA budget refusal status and the raised limit\n\nAssert ERR_UNSUPPORTED on the whole-computation budget refusal, and keep the\nformer 900 x 900 probe as a positive regression: it exceeded the pre-1.3.12\n2.4e9 limit and must now be accepted. Addresses the release review's test\ngaps.\n\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n",
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  "_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": "area_normalization_transform",
      "title": "Area normalization transform",
      "topics": [
        "area_normalization_transform"
      ]
    },
    {
      "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": "detrend_transform",
      "title": "Polynomial detrend transform",
      "topics": [
        "detrend_transform"
      ]
    },
    {
      "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": "emsc_fit",
      "title": "Fit Extended Multiplicative Scatter Correction",
      "topics": [
        "emsc_fit"
      ]
    },
    {
      "page": "emsc_transform",
      "title": "Apply fitted Extended Multiplicative Scatter Correction",
      "topics": [
        "emsc_transform"
      ]
    },
    {
      "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": "kennard_stone_split",
      "title": "Kennard-Stone train/test split",
      "topics": [
        "kennard_stone_split",
        "n4m_model_selection_kennard_stone"
      ]
    },
    {
      "page": "kernel_pls_fit",
      "title": "Non-linear kernel PLS (Rosipal & Trejo 2001).",
      "topics": [
        "kernel_pls_fit"
      ]
    },
    {
      "page": "local_snv_transform",
      "title": "Local Standard Normal Variate transform",
      "topics": [
        "local_snv_transform"
      ]
    },
    {
      "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": "msc_fit",
      "title": "Fit native Multiplicative Scatter Correction",
      "topics": [
        "msc_fit"
      ]
    },
    {
      "page": "msc_transform",
      "title": "Apply a fitted MSC reference to spectra",
      "topics": [
        "msc_transform"
      ]
    },
    {
      "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": "n4m_affine_fit",
      "title": "Fit and predict a native affine MethodResult regressor",
      "topics": [
        "n4m_affine_fit",
        "predict.n4m_affine_fit"
      ]
    },
    {
      "page": "n4m_affine_model_export",
      "title": "Export a fitted affine MethodResult predictor",
      "topics": [
        "n4m_affine_model_export"
      ]
    },
    {
      "page": "n4m_affine_supported_methods",
      "title": "List MethodResult regressors marked for native affine conversion",
      "topics": [
        "n4m_affine_supported_methods"
      ]
    },
    {
      "page": "n4m_augmentation_apply",
      "title": "Apply a seeded native X-only training augmentation",
      "topics": [
        "n4m_augmentation_apply"
      ]
    },
    {
      "page": "n4m_estimator_role_constructors",
      "title": "Constructors of the generic native estimator roles",
      "topics": [
        "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"
      ]
    },
    {
      "page": "n4m_estimator_roles",
      "title": "Generic native estimator roles",
      "topics": [
        "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"
      ]
    },
    {
      "page": "pls4all_fit",
      "title": "Fit a PLS regression model via the libn4m C ABI.",
      "topics": [
        "n4m_fit"
      ]
    },
    {
      "page": "n4m_manifest",
      "title": "Native manifest and method lookup",
      "topics": [
        "n4m_constructor",
        "n4m_manifest",
        "n4m_manifest_json"
      ]
    },
    {
      "page": "n4m_method",
      "title": "Low-level n4m method dispatcher.",
      "topics": [
        "n4m_method"
      ]
    },
    {
      "page": "n4m_model_descriptor",
      "title": "Inspect a fully validated N4MM model descriptor",
      "topics": [
        "n4m_model_descriptor"
      ]
    },
    {
      "page": "n4m_model_export",
      "title": "Export a fitted n4m model as portable N4MM bytes",
      "topics": [
        "n4m_model_export"
      ]
    },
    {
      "page": "n4m_model_import",
      "title": "Import a fitted model from portable N4MM bytes",
      "topics": [
        "n4m_model_import"
      ]
    },
    {
      "page": "n4m_model_import_linear_predictor",
      "title": "Import an affine predictor as a portable N4MM model",
      "topics": [
        "n4m_model_import_linear_predictor"
      ]
    },
    {
      "page": "n4m_model_inspect",
      "title": "Inspect portable N4MM model metadata",
      "topics": [
        "n4m_model_inspect"
      ]
    },
    {
      "page": "n4m_model_pipeline_info",
      "title": "Inspect the native preprocessing state embedded in N4MM bytes",
      "topics": [
        "n4m_model_pipeline_info"
      ]
    },
    {
      "page": "n4m_multimodal_classifier",
      "title": "Native Raw Multimodal PLS-logistic Classifier",
      "topics": [
        "n4m_multimodal_classifier",
        "n4m_multimodal_classifier_from_state"
      ]
    },
    {
      "page": "n4m_multimodal_pipeline",
      "title": "Complete Native Raw Multimodal Pipeline",
      "topics": [
        "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"
      ]
    },
    {
      "page": "n4m_optimizer",
      "title": "Native hyperparameter optimization from R",
      "topics": [
        "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"
      ]
    },
    {
      "page": "pls4all_predict",
      "title": "Predict with a fitted n4m model.",
      "topics": [
        "n4m_predict"
      ]
    },
    {
      "page": "n4m_preprocess_pipeline",
      "title": "Generic native preprocessing pipeline",
      "topics": [
        "n4m_preprocess_capabilities",
        "n4m_preprocess_export",
        "n4m_preprocess_fit",
        "n4m_preprocess_import",
        "n4m_preprocess_plan",
        "n4m_preprocess_step",
        "n4m_preprocess_transform"
      ]
    },
    {
      "page": "n4m_procedures",
      "title": "Generic native procedures",
      "topics": [
        "n4m_augment",
        "n4m_augment.n4m_augmenter",
        "n4m_procedures",
        "n4m_run",
        "n4m_run.n4m_procedure",
        "n4m_split",
        "n4m_split.n4m_splitter"
      ]
    },
    {
      "page": "n4m_role_pipeline",
      "title": "Native role pipelines",
      "topics": [
        "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"
      ]
    },
    {
      "page": "n4m_splitter_run",
      "title": "Run one of nine native sample splitters",
      "topics": [
        "n4m_splitter_run"
      ]
    },
    {
      "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",
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