--- warning: DO NOT CHANGE THIS MANUALLY, THIS IS GENERATED BY https://github.com/duckdb/community-extensions repository, check README there title: ducksmiles excerpt: | DuckDB Community Extensions Cheminformatics and CPU numerical analytics for DuckDB - molecular formats, descriptors, fingerprints, similarity, regression, statistical inference, experimental design, time series, reliability and model evaluation from SQL extension: name: ducksmiles description: Cheminformatics and CPU numerical analytics for DuckDB - molecular formats, descriptors, fingerprints, similarity, regression, statistical inference, experimental design, time series, reliability and model evaluation from SQL version: 0.6.4 language: C++ build: cmake requires_toolchains: rust test_config: >- {"test_env_variables":{"DUCKDB_TEST_ON_INIT":"LOAD '{BUILD_DIR}/extension/ducksmiles/ducksmiles.duckdb_extension';","T":"\"test/*\" --test-flags='--require ducksmiles --require json' --fail-require-skip","DUCKDB_TEST_STATICALLY_LOADED_EXTENSIONS":"['core_functions','json']"}} license: MIT maintainers: - nkwork9999 repo: github: nkwork9999/duckSMILES ref: 2d4a5201c71c15828c7be8e2fc7e1d8b12a0cb85 docs: hello_world: | -- Parse SMILES and get molecular formula SELECT mol_formula('CCO'); -- C2H6O -- Calculate molecular weight SELECT round(mol_weight('c1ccccc1'), 2); -- 78.11 -- Wildman-Crippen LogP and molar refractivity (match RDKit) SELECT round(logp_crippen('CCO'), 4), round(mol_mr('c1ccccc1'), 3); -- -0.0014, 26.442 -- QED drug-likeness score (RDKit Chem.QED, weights_mean) SELECT round(qed('CC(=O)Oc1ccccc1C(=O)O'), 3); -- 0.550 (aspirin) -- Drug-likeness rule panels + toxicophore alerts SELECT lipinski_violations('CC(=O)Oc1ccccc1C(=O)O'), druglikeness_pass('CC(=O)Oc1ccccc1C(=O)O', 'veber'), structural_alerts_json('O=Cc1ccccc1'); -- 0, 1, ["aldehyde","aldehyde_any"] -- Morgan/ECFP fingerprint + similarity between two BLOBs SELECT round(tanimoto_bit(morgan_fp_bits('CCO'), morgan_fp_bits('CCN')), 4); -- 0.3333 -- In-silico docking: SMILES ligand vs PDB receptor -> ranked poses (JSON) SELECT dock('CCO', :receptor_pdb, 1.7,0.2,0.5, 8,8,8, 20, 42); -- Retrospective virtual-screening enrichment over a docked, labelled library SELECT roc_auc(list(score), list(is_active)), enrichment_factor(list(score), list(is_active), 0.01), bedroc(list(score), list(is_active), 20.0) FROM docked_library; -- CPU PCA on a numeric matrix, returning versioned JSON SELECT chem_pca([[1.0,2.0],[2.0,4.0],[3.0,6.0]], [1.0,0.0]); -- Convert SMILES to SELFIES (ML-friendly notation) SELECT smiles_to_selfies('CCO'); extended_description: | Cheminformatics toolkit for DuckDB - analyze molecular structures directly from SQL without leaving your database. Pure Rust implementation with no external chemistry library dependencies (no RDKit required). **Supported Formats:** - SMILES: validation, formula, weight, atom/bond counts, canonicalization, aromaticity - InChI/InChIKey: layer extraction, stereochemistry detection, skeleton matching - MOL/SDF: V2000/V3000 block parsing, 3D geometry, molecule counting - PDB/CIF/XYZ: protein structure analysis (atom, chain, residue, model counts) - SELFIES: bidirectional SMILES-SELFIES conversion for ML pipelines **435 SQL overloads**, including the 156 molecular overloads and 279 CPU analytics functions. Molecular methods cover property extraction, format conversion, structural comparison, fingerprinting, similarity search, drug-likeness / toxicophore filtering, and structure-based virtual screening. Function descriptions, meaningful argument names, runnable examples and categories are discoverable through `duckdb_functions()` for every overload. Filter with `list_contains(categories, 'ducksmiles')` after loading the extension. **Descriptors (RDKit-faithful ports):** - 21 structural-profile functions: fragments, formal charge, hydrogen and bond counts, ring membership, elemental counts, fractions, isotope-aware heavy-atom mass and mean heavy-atom degree. A pinned RDKit 2025.09.6 reference suite checks 4,510 inputs; this is finite-corpus validation, not a guarantee of full RDKit parser/sanitizer equivalence. - `logp_crippen()` / `mol_mr()` - Wildman-Crippen LogP and molar refractivity. - `tpsa()` - Ertl topological polar surface area. - `qed()` - QED drug-likeness (Bickerton 2012), a port of RDKit `Chem.QED`. - Lipinski set: `num_h_donors()`, `num_h_acceptors()`, `num_rotatable_bonds()`, `fraction_csp3()`, `num_heteroatoms()`, and the full ring-count descriptor family. **Mass context and native graphs:** - Isotope-labelled formula masses, average formula weights, composition JSON, conservative CHNOFClBrI DBE and signed mass-error ppm calculations. - MOL/SDF parsing preserves unnamed records, sparse V3000 IDs and double-precision geometry; malformed block boundaries and bond references are rejected. - `formula_exact_mass()` parses supported flat formulas strictly. - `adduct_neutral_mass()` and `adduct_precursor_mz()` perform versioned arithmetic for 29 explicit adduct labels, including signed electron corrections. - `mol_graph_json()` exports a versioned native atom/bond graph in input order with explicit hydrogen vertices, isotope, charge and raw stereo tokens. This sidecar is not pretrained-model tensors or full RDKit sanitization. - Unsupported or malformed inputs return NULL. **Fingerprints & similarity:** - `morgan_fp_bits()` (Morgan/ECFP-style, radius 2 / 2048-bit default) and `maccs_keys()` (167-bit MACCS). Morgan bit positions are not claimed to be identical to RDKit's MorganGenerator. - Full bit-fingerprint similarity family over raw BLOBs, faithful to RDKit `DataStructs/BitOps.cpp`: `tanimoto_bit`, `dice_bit`, `cosine_bit`, `kulczynski_bit`, `sokal_bit`, `mcconnaughey_bit`, `asymmetric_bit`, `braun_blanquet_bit`, `russel_bit`, and asymmetric `tversky_bit(a, b, alpha, beta)`. **Structure tools:** SMARTS substructure search/counting, Bemis-Murcko and generic scaffolds, scaffold networks, ring-system extraction, maximum common substructure (MCS), and MolHash standardization. **Drug-likeness & toxicophore filtering:** - `admet_json()` - a full report: descriptors plus six drug-likeness rule panels. - `druglikeness_pass(smiles, rule)` for Lipinski, Veber, Ghose, Egan, Muegge and Lead-likeness; `lipinski_violations()` for the Rule-of-Five count. - `structural_alerts_json()` / `structural_alert_count()` over a curated Brenk / PAINS reactive-group toxicophore catalogue. **In-silico docking pipeline (structure-based virtual screening, end-to-end in SQL):** - `smiles_to_pdbqt()` - 3D conformer generation (distance geometry + an L-BFGS- minimised lite force field with aromatic-ring planarity) emitted as PDBQT. - `prepare_receptor(pdb, ph)` - pH-dependent protonation states + polar-hydrogen addition, so the receptor can donate H-bonds. - `dock(smiles, pdb, cx, cy, cz, sx, sy, sz, n_runs, seed [, ph])` - flexible torsion-tree docking with an AutoDock-Vina-style scoring function and 3D affinity maps; returns ranked poses with coordinates as JSON. - Retrospective-validation harness: `roc_auc`, `enrichment_factor(fraction)`, `bedroc(alpha)` to measure how well a docked library enriches known actives. **CPU numerical analytics:** - 267 functions for descriptive/inferential statistics, regression, mixed/latent models, IRT/SEM, causal evaluation, experimental design, quality/reliability, time series, functional preprocessing, numeric text analysis and spatial models. - Numeric matrices and ordered parameter lists; versioned JSON results, reusable fitted models, and NULL for invalid or resource-limited inputs. No GPU or Python runtime is required. Existing molecular methods remain available. - Complete SQL examples, parameter contracts, validation evidence and remaining gaps are in the [README](https://github.com/nkwork9999/duckSMILES#cpu-analytics-complete-sql-api-reference). These are bounded partial method contracts; full JMP equivalence is not claimed. The new bounded CPU methods include censored regression, estimated spatial covariance, static SVG histograms/box plots and ordered categorical ML with saved prediction. JMP default Lasso, sandwich inference and full plot options remain explicitly incomplete in the source README. **Architecture:** Rust (core logic, zero external chemistry crates) + C++ (DuckDB integration via FFI). extension_star_count: 4 extension_star_count_pretty: 4 extension_download_count: 902 extension_download_count_pretty: 902 image: '/images/community_extensions/social_preview/preview_community_extension_ducksmiles.png' layout: community_extension_doc --- ### Compatibility Matrix This table covers all DuckDB releases that have not reached [end-of-life](https://duckdb.org/release_calendar), including LTS releases, and shows the 2 most recent patch releases of each. Older patch releases are not listed, but the extension may be available for them as well.
| Platform | 1.4.4 | 1.4.5 | 1.5.5 | 1.5.6 | 2.0.0 | | :--- | :---: | :---: | :---: | :---: | :---: | | Linux AMD64 | ❌ | ❌ | ✅ | ✅ | ✅ | | Linux ARM64 | ❌ | ❌ | ✅ | ✅ | ✅ | | macOS AMD64 | ❌ | ❌ | ✅ | ✅ | ✅ | | macOS ARM64 | ❌ | ❌ | ✅ | ✅ | ✅ | | Windows AMD64 | ❌ | ❌ | ✅ | ✅ | ✅ | | Windows ARM64 | ❌ | ❌ | ❌ | ❌ | ❌ | | WebAssembly | ❌ | ❌ | ✅ | ✅ | ✅ | ### Installing and Loading ```sql INSTALL {{ page.extension.name }} FROM community; LOAD {{ page.extension.name }}; ``` {% if page.docs.hello_world %} ### Example ```sql {{ page.docs.hello_world }}``` {% endif %} {% if page.docs.extended_description %} ### About {{ page.extension.name }} {{ page.docs.extended_description }} {% endif %} ### Added Functions
| function_name | function_type | description | comment | examples | |---------------------------------------|---------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | add_hydrogens | scalar | Return SMILES with implicit hydrogens expanded to explicit atoms. | NULL | [add_hydrogens('CCO')] | | adduct_neutral_mass | scalar | Recover neutral M from precursor m/z for an explicitly supported adduct. NULL for unknown labels or nonpositive/nonfinite input or output. | NULL | [adduct_neutral_mass(195.087652, '[M+H]+')] | | adduct_precursor_mz | scalar | Calculate precursor m/z from neutral M for an explicitly supported adduct, including signed-charge electron correction and molecule count. NULL for invalid input; no adduct inference is performed. | NULL | [adduct_precursor_mz(194.080376, '[M+H]+')] | | admet_json | scalar | Return native physicochemical descriptors, drug-likeness rule panels and structural alerts as JSON. | NULL | [admet_json('CCO')] | | asymmetric_bit | scalar | Calculate asymmetric (intersection divided by the smaller on-bit count) similarity between equal-length BLOB bit fingerprints. | NULL | [asymmetric_bit(morgan_fp_bits('CCO'), morgan_fp_bits('CCN'))] | | bedroc | scalar | Calculate BEDROC early-recognition performance for ascending screening scores and true active labels. | NULL | [bedroc([-3.0, -2.0, -1.0, 0.0], [true, false, true, false], 20.0)] | | braun_blanquet_bit | scalar | Calculate Braun-Blanquet similarity between equal-length BLOB bit fingerprints. | NULL | [braun_blanquet_bit(morgan_fp_bits('CCO'), morgan_fp_bits('CCN'))] | | canonical_smiles | scalar | Return a deterministic normalized SMILES for the supported molecular graph subset. | NULL | [canonical_smiles('CCO')] | | chem_acceptance_oc | scalar | Single binomial acceptance-sampling OC probabilities: defect probability rows; [n,c]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_acceptance_oc([[0.01],[0.1]], [10.0,0.0])] | | chem_acf | scalar | Biased sample ACF: ordered nonconstant scalar rows; [max_lag0..min(n-1,10000)]; <=50000000 work units. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_acf([[2.0],[4.0],[3.0],[5.0],[7.0],[6.0],[8.0],[9.0]], [3.0])] | | chem_acquisition | scalar | Expected improvement: rows [mean,sigma], parameters [best,minimize,xi]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_acquisition([[2.0,1.0]], [2.0,0.0,0.0])] | | chem_adjust_pvalues | scalar | Multiplicity adjustment: rows [p], parameters [0 Holm/1 BH]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_adjust_pvalues([[0.01],[0.04],[0.03]], [1.0])] | | chem_admixture_predict | scalar | Infer ancestry of new diploid rows with saved fixed allele frequencies; exact marker_admixture predict_parameters; each starting ancestry weight strictly positive. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_admixture_predict([[0.0,2.0],[1.0,1.0]], [2.0,2.0,10000.0,1e-10,1e-8,0.5,0.5,0.1,0.9,0.9,0.1])] | | chem_aft_exponential | scalar | Right-censored exponential AFT: [features...,positive_time,event0/1]; [ridge=0,maxiter=200,tolerance=1e-8]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_aft_exponential([[1.0,1.0],[2.0,1.0],[3.0,0.0],[4.0,1.0],[5.0,0.0]], [])] | | chem_aft_lognormal | scalar | Right-censored lognormal AFT: [features...,positive_time,event0/1]; [ridge=0,maxiter=200,tolerance=1e-8]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_aft_lognormal([[1.0,1.0],[2.0,1.0],[3.0,0.0],[4.0,1.0],[5.0,0.0]], [])] | | chem_aft_predict | scalar | Saved AFT survival/quantile prediction: [features...,query] rows; [mode0survival/1quantile,predict_parameters...]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_aft_predict([[0.0],[5.0],[10.0]], [0.0,0.0,0.0,0.0,0.0,1.6094379124341003])] | | chem_aft_weibull | scalar | Right-censored weibull AFT: [features...,positive_time,event0/1]; [ridge=0,maxiter=200,tolerance=1e-8]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_aft_weibull([[1.0,1.0],[2.0,1.0],[3.0,0.0],[4.0,1.0],[5.0,0.0]], [])] | | chem_agreement_kappa | scalar | Unweighted two-rater Cohen kappa from a square count table; []. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_agreement_kappa([[20.0,5.0],[5.0,20.0]], [])] | | chem_aipw_effect | scalar | Canonical AIPW ATE/ATT scores with influence SE: [outcome,treatment,propensity,m0,m1]; [estimand0ATE/1ATT=0,explicit_clip=0]. External nuisance fitting required. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_aipw_effect([[4.0,1.0,0.5,2.0,4.0],[6.0,1.0,0.75,3.0,6.0],[1.0,0.0,0.25,1.0,3.0],[3.0,0.0,0.5,3.0,5.0]], [])] | | chem_analyze | scalar | Run a named numerical analytics operation. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_analyze('acquisition', [[2.0,1.0]], [2.0,0.0,0.0])] | | chem_ar_model | scalar | Conditional Gaussian autoregression via QR with intercept: ordered scalar rows; [order1..32,horizon1..10000]; requires identifiable residual variance. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_ar_model([[2.0],[4.0],[3.0],[5.0],[7.0],[6.0],[8.0],[9.0]], [2.0,2.0])] | | chem_arima | scalar | Conditional-sum-of-squares seasonal ARIMA: ordered scalar rows; [p0..3,d0..2,q0..3,horizon0..1000,constant=1,maxiter=1000,tolerance=1e-8,P0..1=0,D0..2=0,Q0..1=0,season0or2..52=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_arima([[2.0],[4.0],[3.0],[5.0],[7.0],[6.0],[8.0],[9.0]], [0.0,1.0,0.0,2.0])] | | chem_arima_forecast_intervals | scalar | Conditional normal ARIMA forecast intervals from supplied means and A/C/difference polynomials: [confidence,sigma2,degreeA,degreeC,nD,A...,C...,difference_lags...],h<=1000. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_arima_forecast_intervals([[10.0],[10.0],[10.0]], [0.95,2.0,0.0,0.0,1.0,1.0,1.0,1.0])] | | chem_arima_prewhitening | scalar | Input-only exact stationary ARMA prewhitening: [X,Y] rows; [n_train,p<=2,q<=2,maxlag,maxiter,tol]. Apply the same innovations operator to both series; suffix ignored. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_arima_prewhitening([[-1.0,0.0],[1.0,1.0],[-1.0,2.0],[1.0,3.0],[-1.0,4.0],[1.0,5.0],[-1.0,6.0],[1.0,7.0],[-1.0,8.0],[1.0,9.0],[-1.0,10.0],[1.0,11.0]], [12.0,0.0,0.0,2.0,100.0,1e-08])] | | chem_assay_normalize | scalar | Seven assay row normalizations: [method0standardize/1CPM/2RPKM/3TPM/4CLR/5percent/6ratio,method-specific args]. Samples rows/features columns; lengths in base pairs. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_assay_normalize([[1.0,3.0],[2.0,6.0]], [3.0,1000.0,2000.0])] | | chem_association_rules | scalar | Binary transaction rows/items columns,including empty baskets; [minimum_support=.1,minimum_confidence=.4,minimum_lift=1.2,max_antecedents=3,max_rule_size=4,max_rules=10000]. Complete Apriori within explicit limits,arbitrary disjoint consequents. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_association_rules([[1.0,1.0,0.0],[1.0,1.0,1.0],[1.0,0.0,1.0],[0.0,1.0,0.0],[0.0,0.0,0.0]], [0.4,0.6,1.5,1.0,2.0])] | | chem_balanced_incomplete_block | scalar | Exact balanced incomplete blocks: distinct treatments<=9; [block_size,lambda=1,seed=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_balanced_incomplete_block([[0.0],[1.0],[2.0],[3.0],[4.0],[5.0],[6.0]], [3.0,1.0,7.0])] | | chem_basket_affinity | scalar | Binary transaction rows,<=32items; []. All distinct pairs: support,directional confidence,lift,Jaccard,leverage; undefined divisions JSONnull. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_basket_affinity([[1.0,1.0,0.0],[1.0,1.0,1.0],[1.0,0.0,1.0],[0.0,1.0,0.0],[0.0,0.0,0.0]], [])] | | chem_binary_metrics | scalar | [outcome0/1,probability0..1,optional_weight>=0]; [threshold=.5,probability_clip=1e-15]. Inclusive threshold, clipped natural-log loss, Brier and confusion metrics. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_binary_metrics([[0.0,0.1],[1.0,0.4],[0.0,0.4],[1.0,0.8]], [0.4])] | | chem_bootstrap_mean | scalar | IID percentile bootstrap mean: >=2 scalar rows; [replicates2..100000,confidence=.95,seed=0]; <=20000000 resampled values. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_bootstrap_mean([[1.0],[2.0],[3.0]], [100.0,0.95,42.0])] | | chem_box_behnken | scalar | Box-Behnken two-factor-edge design: 3..64 [low,high] factor bounds; [center_points0..100000=1]; <=100000 runs and1000000 output cells. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_box_behnken([[-1.0,1.0],[-1.0,1.0],[-1.0,1.0]], [])] | | chem_box_cox | scalar | Box-Cox transform and profile likelihood: >=2 varying positive scalar rows; [lambda] or [] to estimate a bracketed finite optimum. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_box_cox([[1.0],[2.0],[4.0]], [0.0])] | | chem_bspline_basis | scalar | Clamped Cox-de Boor B-spline basis: [x] rows; [degree1..3,full_clamped_knots...]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_bspline_basis([[0.0],[0.5],[1.0],[2.0]], [3.0,0.0,0.0,0.0,0.0,1.0,2.0,2.0,2.0,2.0])] | | chem_c_chart | scalar | Poisson count control chart: count rows; []. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_c_chart([[4.0],[6.0]], [])] | | chem_calibration_curve | scalar | [outcome0/1,probability0..1,optional_weight>=0]; [bins1..1000=10]. Equal-width bins include probability1 in last bin; empty-bin means null; ECE and maximum error. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_calibration_curve([[0.0,0.0],[1.0,0.5],[1.0,1.0]], [4.0])] | | chem_canonical_correlation | scalar | Two-block sample-covariance whitening with <=32 variables per block: numeric [X...,Y...] rows; [x_columns,dimensions=min(p,q),standardized_ridge=0]; no MANOVA inference. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_canonical_correlation([[-1.0,-2.0],[0.0,0.0],[1.0,2.0],[2.0,4.0]], [1.0])] | | chem_canonical_correlation_inference | scalar | Sequential Wilks/Bartlett asymptotic chi-square: sorted unregularized correlations as one-column rows; [n_samples>p+q,p1..64,q1..64]; all min(p,q) correlations required; not raw-data fitting. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_canonical_correlation_inference([[0.8],[0.6]], [100.0,2.0,2.0])] | | chem_causal_balance | scalar | Raw-score overlap and weighted covariate balance: [features...,treatment,propensity]; [estimand0ATE/1ATT=0,explicit_clip=0]. Fixed baseline pooled SD, ESS; both groups>=2. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_causal_balance([[0.0,1.0,0.5],[2.0,1.0,0.75],[1.0,0.0,0.25],[3.0,0.0,0.5]], [])] | | chem_censored_beta | scalar | Beta joint censored maximum likelihood: [features...,lower,upper,kind0exact/1left/2right/3interval,frequency]; [maxiter=400,tol=1e-7,confidence=.95,optional raw starts]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_censored_beta([[0.15,0.15,0,1],[0.25,0.25,0,1],[0.4,0.4,0,1],[0.55,0.55,0,1],[0.75,0.75,0,1]], [])] | | chem_censored_exponential | scalar | Exponential joint censored maximum likelihood: [features...,lower,upper,kind0exact/1left/2right/3interval,frequency]; [maxiter=400,tol=1e-7,confidence=.95,optional raw starts]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_censored_exponential([[0.4,0.4,0,1],[0.8,0.8,0,1],[1.2,1.2,0,1],[2.0,2.0,0,1],[3.0,3.0,0,1]], [])] | | chem_censored_gamma | scalar | Gamma joint censored maximum likelihood: [features...,lower,upper,kind0exact/1left/2right/3interval,frequency]; [maxiter=400,tol=1e-7,confidence=.95,optional raw starts]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_censored_gamma([[0.4,0.4,0,1],[0.8,0.8,0,1],[1.2,1.2,0,1],[2.0,2.0,0,1],[3.0,3.0,0,1]], [])] | | chem_censored_lognormal | scalar | Lognormal joint censored maximum likelihood: [features...,lower,upper,kind0exact/1left/2right/3interval,frequency]; [maxiter=400,tol=1e-7,confidence=.95,optional raw starts]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_censored_lognormal([[0.4,0.4,0,1],[0.8,0.8,0,1],[1.2,1.2,0,1],[2.0,2.0,0,1],[3.0,3.0,0,1]], [])] | | chem_censored_normal | scalar | Normal joint censored maximum likelihood: [features...,lower,upper,kind0exact/1left/2right/3interval,frequency]; [maxiter=400,tol=1e-7,confidence=.95,optional raw starts]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_censored_normal([[-2,-2,0,1],[-1,-1,0,1],[0,0,0,1],[1,1,0,1],[2,2,0,1]], [])] | | chem_censored_predict | scalar | Saved censored-family mean,CDF,survival,density,quantile and observed-information delta uncertainty: [features...,query,probability] and complete predict_parameters. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_censored_predict([[0.0,0.5],[1.0,0.975]], [1.0,0.0,0.0,0.95,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])] | | chem_censored_weibull | scalar | Weibull joint censored maximum likelihood: [features...,lower,upper,kind0exact/1left/2right/3interval,frequency]; [maxiter=400,tol=1e-7,confidence=.95,optional raw starts]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_censored_weibull([[0.4,0.4,0,1],[0.8,0.8,0,1],[1.2,1.2,0,1],[2.0,2.0,0,1],[3.0,3.0,0,1]], [])] | | chem_central_composite_design | scalar | Central composite design: rows [low,high], parameters [alpha,center_points]. Axial points may exceed bounds. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_central_composite_design([[10.0,20.0],[1.0,3.0]], [1.0,1.0])] | | chem_cfa_model | scalar | Gaussian simple-structure CFA: rows[S_MLrow(p),mean]; [N,q,correlated,maxiter,tolerance,assignments(p)]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_cfa_model([[1.0,0.7200000000000001,0.5599999999999999,0.23799999999999996,0.20999999999999996,0.242,10.0],[0.7200000000000001,1.0,0.63,0.26775,0.23625000000000002,0.20475000000000002,-2.0],[0.5599999999999999,0.63,1.0,0.20824999999999996,0.18374999999999997,0.15924999999999997,0.0],[0.238,0.26775,0.20825,1.0,0.6375,0.5525,5.0],[0.20999999999999996,0.23624999999999996,0.18374999999999997,0.6375,1.0,0.48750000000000004,1.0],[0.242,0.20475,0.15924999999999997,0.5525,0.48750000000000004,1.0,-3.0]], [300.0,2.0,1.0,500.0,1e-08,0.0,0.0,0.0,1.0,1.0,1.0])] | | chem_cfa_scores | scalar | Saved CFA factor scores: observation rows; [method0regression/1Bartlett,score_parameters...]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_cfa_scores([[2.6,3.4,4.8]], [1.0,3.0,1.0,1.0,2.0,3.0,0.8,0.7,0.9,1.0,0.36,0.51,0.19])] | | chem_cohen_d | scalar | Pooled-SD Cohen d and exact gamma-ratio Hedges g: [value,group0/1], >=2 observations per group; []. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_cohen_d([[1.0,0.0],[2.0,0.0],[3.0,1.0],[5.0,1.0]], [])] | | chem_confusion_matrix | scalar | [actual_integer_label,predicted_integer_label,optional_weight>=0]; []; <=256 observed/predicted levels; actual rows, predicted columns. Undefined class precision/recall null; macro F1 includes union of positive-weight levels. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_confusion_matrix([[10.0,10.0],[10.0,20.0],[20.0,20.0],[30.0,20.0]], [])] | | chem_contingency_test | scalar | Pearson chi-square independence test, expected counts and Cramer V: observed count matrix; []. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_contingency_test([[10.0,20.0],[20.0,10.0]], [])] | | chem_correlation_matrix | scalar | Pearson correlation matrix: >=2 rows, <=256 finite nonconstant features; []; centered products and bounded work. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_correlation_matrix([[1.0,2.0],[2.0,1.0],[3.0,6.0]], [])] | | chem_correspondence_analysis | scalar | Simple contingency-table SVD: nonnegative 2..64 by2..64 counts with positive margins; [dimensions=min(2,r-1,c-1),scaling0principal/1symmetric=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_correspondence_analysis([[20.0,10.0],[10.0,20.0]], [1.0])] | | chem_covariance_matrix | scalar | Centered covariance matrix: <=256 finite features; [sample1/0population=1], sample mode requires >=2 rows; <=100000000 numerical work units. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_covariance_matrix([[1.0,2.0],[2.0,1.0],[3.0,6.0]], [])] | | chem_cox_ph | scalar | Cox proportional hazards with <=32 features and bounded event work: [features...,time>=0,event0/1]; [ties0Breslow/1Efron=1,ridge=0,maxiter1..1000=100,tolerance=1e-8]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_cox_ph([[0.0,1.0,1.0,1.0],[1.0,0.0,1.0,1.0],[0.0,0.0,2.0,0.0],[2.0,1.0,3.0,1.0],[1.0,2.0,3.0,0.0],[2.0,0.0,4.0,1.0],[0.0,2.0,4.0,0.0],[1.0,1.0,5.0,1.0],[2.0,2.0,6.0,1.0],[0.0,1.0,6.0,0.0],[2.0,0.0,7.0,1.0],[1.0,2.0,8.0,0.0]], [])] | | chem_cox_predict | scalar | Centered Cox survival prediction, <=32 features: feature rows; [baseline_survival0..1,coefficients(p),feature_origins(p),mean_offsets(p)]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_cox_predict([[0.0],[1.0],[2.0]], [0.8,0.6931471805599453,0.0,0.0])] | | chem_cross_correlation | scalar | CCF for complete input/output rows: [maxlag<=500,normalization0global/1overlapPearson]; positive lag means input leads output; +/-2 white-noise SE bands. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_cross_correlation([[1.0,0.0],[0.0,1.0],[-1.0,0.0],[0.0,-1.0]], [2.0,0.0])] | | chem_cross_validate | scalar | Fold-local CV: rows [features...,target,fold], [method 0 ridge/1 PLS,hyperparameter]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_cross_validate([[0.0,1.0,0.0],[1.0,3.0,0.0],[2.0,5.0,1.0],[3.0,7.0,1.0]], [0.0,1.0])] | | chem_cumulative_damage_fit | scalar | Step-stress cumulative-exposure ML: [lo_time,hi_time,type,pattern_id]; [distribution,relation,continuation,maxiter,tolerance,pattern_count,patterns...]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_cumulative_damage_fit([[1.0,1.0,0.0,1.0],[3.0,3.0,0.0,1.0],[2.0,2.0,0.0,2.0],[6.0,6.0,0.0,2.0]], [0.0,0.0,1.0,300.0,1e-09,2.0,1.0,1.0,10.0,0.0,2.0,1.0,10.0,1.0])] | | chem_cumulative_damage_predict | scalar | Saved cumulative-exposure reliability: [time,pattern_id,probability]; unchanged cumulative_damage_fit.predict_parameters. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_cumulative_damage_predict([[4.0,7.0,0.5]], [1.0,0.0,0.0,1.0,0.0,0.6931471805599453,1.0,1.0,7.0,2.0,2.0,0.0,3.0,1.0])] | | chem_curve_derivative | scalar | Finite curve derivatives: equally spaced curve rows; [order=1,spacing=1]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_curve_derivative([[0.0,1.0,4.0,9.0,16.0]], [2.0,0.5])] | | chem_curve_fit | scalar | Nonlinear residual least squares, 10 curve families: [x,y] rows; [family0..9,max_iterations=300,tolerance=1e-9,optional_initial_coefficients...]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_curve_fit([[0.0,2.03],[0.5,2.64],[1.0,3.7],[1.5,4.88],[2.0,6.68],[2.5,8.94]], [0.0,500.0,1e-10,1.8,0.4])] | | chem_curve_predict | scalar | Saved nonlinear curve prediction: [x] rows; fit.predict_parameters=[family,coefficients...]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_curve_predict([[0.0],[1.0]], [0.0,2.0,0.6])] | | chem_cusum_chart | scalar | Standardized two-sided CUSUM: ordered scalar rows; [k,h,known_center,known_sigma]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_cusum_chart([[1.0],[2.0],[3.0]], [0.5,5.0,0.0,1.0])] | | chem_d_optimal_design | scalar | Local D-optimal exchange over supplied candidate rows, with intercept: [n_runs,max_iterations,seed]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_d_optimal_design([[-1.0],[-0.5],[0.5],[1.0]], [2.0,20.0,42.0])] | | chem_dbscan | scalar | Euclidean DBSCAN in original feature units: [eps>=0,min_samples>=1 including self]; <=5000 rows and100000000 work units; noise label -1. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_dbscan([[0.0],[0.1],[0.2],[2.0],[2.1],[2.2],[9.0]], [0.21,3.0])] | | chem_decision_tree | scalar | CART squared-loss/Gini tree with <=64 features: [features...,target], training prefix; [n_train,depth1..16,min_leaf>=1,task0regression/1classification]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_decision_tree([[0.0,1.0],[1.0,3.0],[2.0,5.0],[3.0,7.0],[1.5,0.0]], [4.0,3.0,1.0,0.0])] | | chem_degradation_path | scalar | Transformed fixed-unit linear paths: [time,response,unit_id]; [intercept_mode=0,slope_mode=0,response_transform=0,time_transform=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_degradation_path([[0.0,10.1,1.0],[1.0,7.9,1.0],[2.0,5.9,1.0],[3.0,4.1,1.0],[0.0,12.1,2.0],[1.0,9.9,2.0],[2.0,7.9,2.0],[3.0,6.1,2.0]], [0.0,1.0,0.0,0.0])] | | chem_degradation_predict | scalar | Saved path predictions and threshold inverse confidence sets: [time,unit_id,threshold]; [confidence,side,model...]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_degradation_predict([[2.0,7.0,6.0]], [0.95,0.0,1.0,0.0,0.0,1.0,7.0,10.0,-2.0,0.04,-0.01,0.01,0.16,8.0])] | | chem_design_evaluate | scalar | Full-rank linear-model design diagnostics with added intercept: <=64 model-term columns, n>=terms+1; []; original/coded information matrices and coded spectral condition number. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_design_evaluate([[-1.0,0.0],[0.0,1.0],[1.0,0.0],[2.0,1.0],[-2.0,-1.0]], [])] | | chem_destructive_degradation | scalar | Independent-unit Gaussian/lognormal censored degradation ML: [time,stress,lower,upper,type]; [family=0,time_transform=0,stress_mode=0,maxiter=300,tolerance=1e-8]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_destructive_degradation([[0.0,0.0,10.1,10.1,0.0],[1.0,0.0,7.9,7.9,0.0],[2.0,0.0,5.9,5.9,0.0],[3.0,0.0,4.1,4.1,0.0]], [])] | | chem_destructive_predict | scalar | Saved independent-unit destructive distribution: [time,stress,threshold]; unchanged destructive_degradation.predict_parameters. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_destructive_predict([[5.0,0.0,2.718281828459045]], [1.0,1.0,0.0,0.0,0.5,2.0,1.0,0.0,0.0,-0.2])] | | chem_difference | scalar | Repeated lag differencing: ordered scalar rows; [lag=1,order1..32=1], must leave >=1 observation. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_difference([[2.0],[4.0],[3.0],[5.0],[7.0],[6.0],[8.0],[9.0]], [1.0,1.0])] | | chem_discriminant_analysis | scalar | Prefix-trained LDA/QDA: [features...,integer_label], query labels ignored; [n_train,method0LDA/1QDA=0,diagonal_ridge=0]; suffix posterior probabilities. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_discriminant_analysis([[-2.0,10.0],[0.0,10.0],[2.0,20.0],[4.0,20.0],[-1.0,99.0],[3.0,99.0]], [4.0,0.0,0.0])] | | chem_distance_matrix | scalar | Numeric row vectors,abs<=1e150; [method0Euclidean/1booleanJaccard/2nonnegativeBrayCurtis/3numericGower/4Hamming=0,optional_missing_code]. <=1000rows,100Mpairfeaturework; pairwise available coordinates, no-common errors; Gower constant columns contribute zero. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_distance_matrix([[0.0,1.0],[2.0,1.0]], [0.0])] | | chem_diversity_select | scalar | Farthest-first candidate selection in supplied feature units: [count,existing_prefix_count]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_diversity_select([[0.0],[1.0],[5.0],[10.0]], [2.0,1.0])] | | chem_dynamic_time_warping | scalar | Pairwise DTW distance and path: [value,series_id0or1] rows; [window_radius=unbounded]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_dynamic_time_warping([[1.0,0.0],[2.0,0.0],[3.0,0.0],[1.0,1.0],[1.0,1.0],[2.0,1.0],[3.0,1.0]], [])] | | chem_elastic_net | scalar | Standardized coordinate-descent elastic net: [features...,target]; [lambda,alpha,max_iterations=10000,tolerance=1e-8]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_elastic_net([[0.0,1.0],[1.0,3.0],[2.0,5.0]], [0.1,0.5])] | | chem_ensemble_predict | scalar | Rows of member predictions; [] equal weights or [nonnegative_weight_per_member...]. Up to1024 members, stable normalized weighted average; no fitting or automatic weight selection. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_ensemble_predict([[1.0,3.0],[3.0,7.0]], [1.0,3.0])] | | chem_equivalence_t | scalar | One-sample normal-model TOST equivalence: scalar rows; [lower_margin,upper_margin,alpha=.05]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_equivalence_t([[0.0],[0.1],[-0.1],[0.05]], [-1.0,1.0,0.05])] | | chem_ewma_chart | scalar | EWMA with transient limits: ordered scalar rows; [lambda,L,known_center,known_sigma]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_ewma_chart([[1.0],[2.0],[3.0]], [0.2,3.0,0.0,1.0])] | | chem_exponential_smoothing | scalar | Simple exponential smoothing: ordered scalar rows; [alpha0..1,horizon1..10000], first-observation initialization. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_exponential_smoothing([[2.0],[4.0],[3.0],[5.0],[7.0],[6.0],[8.0],[9.0]], [0.4,2.0])] | | chem_factor_analysis | scalar | Iterated principal-axis factoring, not ML: <=64 variables; [factors,rotation0none/1varimax=1,maxiter1..2000=200,tol=1e-8,prior0ONE/1SMC=1,input0rows/1correlation=0]; bounded work. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_factor_analysis([[1.0,0.64,0.64],[0.64,1.0,0.64],[0.64,0.64,1.0]], [1.0,0.0,500.0,1e-10,1.0,1.0])] | | chem_factor_analysis_ml | scalar | Gaussian ML correlation-factor model with2..24variables: [factors,rotation0none/1varimax=1,maxiter1..1000=200,tol=1e-8,uniqueness_floor=1e-6,input0rows/1correlation=0,n_correlation_samples=0]; bounded local optimizer. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_factor_analysis_ml([[1.0,0.49,0.49,0.49],[0.49,1.0,0.49,0.49],[0.49,0.49,1.0,0.49],[0.49,0.49,0.49,1.0]], [1.0,0.0,300.0,1e-9,1e-6,1.0,200.0])] | | chem_factorial_design | scalar | Full two-level factorial: rows [low,high], parameters [center_points]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_factorial_design([[10.0,20.0],[1.0,3.0]], [1.0])] | | chem_fisher_exact | scalar | Two-sided fixed-marginal Fisher exact test: 2x2 counts; []. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_fisher_exact([[1.0,9.0],[11.0,3.0]], [])] | | chem_fit_exponential | scalar | Zero-location exponential MLE: nonnegative scalar rows with positive total exposure; []; <=100000 observations. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_fit_exponential([[1.0],[2.0],[4.0]], [])] | | chem_fit_gamma | scalar | Zero-location Gamma MLE with bracketed shape solve: varying positive scalar rows; []; <=100000 observations. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_fit_gamma([[1.0],[2.0],[3.0],[5.0]], [])] | | chem_fit_lognormal | scalar | Zero-location lognormal MLE: >=2 varying strictly positive scalar rows; []; <=100000 observations. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_fit_lognormal([[1.0],[2.0],[4.0],[8.0]], [])] | | chem_fit_normal | scalar | Unweighted normal MLE: >=2 nonconstant scalar rows; []; <=100000 observations, absolute values<=1e150. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_fit_normal([[1.0],[2.0],[4.0],[5.0]], [])] | | chem_fit_poisson | scalar | Poisson MLE with full count likelihood: exact nonnegative integer scalar rows<=9007199254740991; []; <=100000 observations. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_fit_poisson([[0.0],[1.0],[2.0],[4.0]], [])] | | chem_fit_weibull | scalar | Zero-location Weibull MLE with bracketed shape solve: varying positive scalar rows; []; <=100000 observations. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_fit_weibull([[1.0],[2.0],[3.0],[5.0]], [])] | | chem_fourier_transform | scalar | Direct discrete Fourier forward/inverse: real curve rows or packed complex rows; [inverse=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_fourier_transform([[1.0,2.0,3.0,4.0]], [])] | | chem_fractional_factorial | scalar | Positive-product fractional factorial: <=64 [low,high] bounds; [base_factors1..16,center_points,generator_masks...]; distinct masks select >=2 base columns; alias resolution not inferred. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_fractional_factorial([[-1.0,1.0],[-1.0,1.0],[-1.0,1.0],[-1.0,1.0]], [3.0,1.0,7.0])] | | chem_functional_arwls | scalar | Baek2015 arPLS/arWLS baseline: [x,y,id,optional role]; [lambda=1e5,weight_tolerance=1e-6,maxiter=100,negative_std_ddof=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_functional_arwls([[0.0,0.0,7.0],[1.0,1.0,7.0],[2.0,0.0,7.0]], [1.0,1e-12,1.0])] | | chem_functional_baseline | scalar | Subtract an explicit baseline function: [x,y,id,optional role]; [baseline_id,outside0null/1constant/2reject=2]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_functional_baseline([[0.0,1.0,9.0,0.0],[2.0,3.0,9.0,0.0],[0.0,4.0,1.0,0.0],[1.0,7.0,1.0,0.0],[2.0,9.0,1.0,0.0],[0.0,8.0,2.0,1.0],[2.0,12.0,2.0,1.0]], [9.0,2.0])] | | chem_functional_bin | scalar | Explicit equal-width function bins: [x,y,id,optional role]; [number,lower,upper,outside0reject/1drop=1]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_functional_bin([[0.0,1.0,1.0],[0.25,3.0,1.0],[1.0,5.0,1.0],[2.0,9.0,1.0],[0.0,2.0,2.0],[2.0,10.0,2.0]], [3.0,0.0,2.0])] | | chem_functional_dtw_matrix | scalar | All-pairs function DTW matrix: [x,y,id,optional role]; [window=-1,normalize0none/1population=0,cost0squared/1absolute=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_functional_dtw_matrix([[0.0,1.0,1.0],[1.0,2.0,1.0],[2.0,3.0,1.0],[0.0,1.0,2.0],[1.0,1.0,2.0],[2.0,2.0,2.0],[3.0,3.0,2.0],[0.0,2.0,3.0],[1.0,2.0,3.0],[2.0,2.0,3.0]], [2.0,0.0,0.0])] | | chem_functional_golden_match | scalar | Supplied golden/reference curve distance ranking: [x,y,id,optional role]; [target_id,metric0RMSE/1MAE/2DTW=0,window=-1,normalize=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_functional_golden_match([[0.0,1.0,9.0],[1.0,2.0,9.0],[2.0,3.0,9.0],[0.0,2.0,1.0],[1.0,3.0,1.0],[2.0,4.0,1.0],[0.0,1.0,2.0],[1.0,2.0,2.0],[2.0,3.0,2.0]], [9.0,0.0,-1.0,0.0])] | | chem_functional_grid | scalar | Per-function irregular common-grid interpolation: [x,y,id,optional role0train/1validation]; [],[size],or[size,lower,upper,outside_policy]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_functional_grid([[4.0,8.0,1.0],[0.0,0.0,1.0],[2.0,4.0,1.0],[1.0,3.0,2.0],[2.0,6.0,2.0],[3.0,9.0,2.0],[4.0,12.0,2.0]], [4.0])] | | chem_functional_pca | scalar | Common-grid functional PCA: curve rows; [components,spacing=1], trapezoid L2 metric. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_functional_pca([[1.0,2.0,3.0],[2.0,4.0,6.0],[3.0,6.0,9.0]], [1.0])] | | chem_functional_snip | scalar | SNIP peak clipping on uniform grids: [x,y,id,optional role]; [max_halfwindow,transform0raw/1Ryan-loglog=1,direction0increasing/1decreasing=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_functional_snip([[0.0,1.0,7.0],[1.0,1.0,7.0],[2.0,9.0,7.0],[3.0,1.0,7.0],[4.0,1.0,7.0]], [1.0,1.0,0.0])] | | chem_functional_thin | scalar | Sorted per-function stride selection: [x,y,id,optional role]; [stride=2,offset=0,keep_last=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_functional_thin([[5.0,5.0,7.0],[4.0,4.0,7.0],[3.0,3.0,7.0],[2.0,2.0,7.0],[1.0,1.0,7.0],[0.0,0.0,7.0]], [2.0,1.0,0.0])] | | chem_gage_rr | scalar | Balanced crossed random-effects ANOVA Gage R&R without pooling: [value,part,operator]; []. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_gage_rr([[1.0,0.0,0.0],[3.0,0.0,0.0],[2.0,0.0,1.0],[4.0,0.0,1.0],[5.0,1.0,0.0],[7.0,1.0,0.0],[6.0,1.0,1.0],[8.0,1.0,1.0]], [])] | | chem_gaussian_mixture | scalar | Full-covariance constrained EM with <=32 features: [components1..32,maxiter1..2000=200,tol=1e-6,standardized_eigenvalue_floor=1e-6,seed=0,n_init1..16=1]; bounded work/model size. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_gaussian_mixture([[-1.0],[0.0],[1.0]], [1.0])] | | chem_gaussian_mixture_predict | scalar | Saved Gaussian mixture probabilities/densities on same-width feature rows: unchanged gaussian_mixture predict_parameters, <=16384 doubles; reuses fit-time preprocessing. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_gaussian_mixture_predict([[-1.0],[0.0],[1.0]], [1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0])] | | chem_gaussian_naive_bayes | scalar | Prefix-trained Gaussian naive Bayes with population class variances: [features...,integer_label]; [n_train,absolute_variance_floor>0=1e-9]; query labels ignored. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_gaussian_naive_bayes([[-2.0,0.0],[0.0,0.0],[2.0,1.0],[4.0,1.0],[-1.0,99.0],[3.0,99.0]], [4.0,1e-09])] | | chem_genomic_blup | scalar | Rows[dosages...,phenotype], query suffix phenotype ignored; [n_train>=2,lambda1e-8..1e12=1,method0VanRaden1/1VanRaden2=0]. <=64markers,10000rows. Fixed residual/genetic variance ratio, intercept + ridge-equivalent marker effects; training-prefix means, no REML/GWAS inference. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_genomic_blup([[0.0,1.0],[1.0,2.0],[2.0,5.0],[1.0,999.0]], [3.0,1.0])] | | chem_genomic_blup_predict | scalar | New dosage rows[0,2] or missing=-1; saved genomic_blup predict_parameters=[p,y_origin,y_mean_offset,training_marker_means(p),marker_effects(p)]. Fixed training preprocessing and missing mean substitution. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_genomic_blup_predict([[0.0],[1.0],[2.0]], [1.0,1.0,1.6666666666666667,1.0,1.6])] | | chem_genreg_ordinal_fit | scalar | Unpenalized ordered cumulative-logit ML with sorted numeric outcome levels,integer replication frequencies,and joint observed-information inference. Fit rows[features...,label,freq],args[maxiter=200,tolerance=1e-7,confidence=.95]. Predict rows[features...,q],args=complete saved vector. p<=16,K<=16,finite sorted labels; no Lasso/JMP sandwich parity. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_genreg_ordinal_fit([[30,1],[10,1],[20,2]], [])] | | chem_genreg_ordinal_predict | scalar | Saved ordinal probability,CDF,mode,quantile-label and observed-information delta prediction with original training transforms. Fit rows[features...,label,freq],args[maxiter=200,tolerance=1e-7,confidence=.95]. Predict rows[features...,q],args=complete saved vector. p<=16,K<=16,finite sorted labels; no Lasso/JMP sandwich parity. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_genreg_ordinal_predict([[0.2],[0.43],[0.81]], [1.0,0.0,3.0,0.95,1.0,10.0,20.0,30.0,-1.0986122886681098,1.0986122886681098,1.3333333333333333,0.4444444444444444,0.4444444444444444,1.3333333333333333])] | | chem_glm_predict | scalar | GLM prediction from saved stable_predict_parameters or [link0identity/1logit/2log,intercept,coefficients...]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_glm_predict([[0.0],[1.0]], [1.0,0.0,1.0])] | | chem_glmm_binomial | scalar | Bernoulli logit Gaussian random-intercept integrated ML: [fixed features...,y,group]; [maxiter=200,tol=1e-6,odd quadrature order=15,variance_upper=100]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_glmm_binomial([[0.0,0.0],[1.0,0.0],[0.0,1.0],[1.0,1.0]], [])] | | chem_glmm_poisson | scalar | Poisson log Gaussian random-intercept integrated ML: [fixed features...,y,group]; [maxiter=200,tol=1e-6,odd quadrature order=15,variance_upper=100]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_glmm_poisson([[1.0,0.0],[3.0,0.0],[1.0,1.0],[3.0,1.0]], [])] | | chem_glmm_predict | scalar | Saved Gaussian random-intercept GLMM prediction: [features...,group] and unchanged predict_parameters; separate conditional,posterior and population means. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_glmm_predict([[7.0],[99.0]], [1.0,1.0,0.0,1.0,3.0,0.5,0.6931471805599453,7.0,0.2,0.2,0.1,-0.34772255750516606,0.2,0.747722557505166,0.16666666666666666,0.6666666666666666,0.16666666666666666])] | | chem_gp_predict | scalar | RBF GP and EI on candidate pool: training prefix then candidates; [n_train,length_scale,signal_variance,noise_variance,minimize,xi]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_gp_predict([[0.0,2.0],[1.0,0.0]], [1.0,1.0,1.0,0.01,0.0,0.0])] | | chem_gradient_boosting | scalar | CPU first-order tree boosting for squared-error/binary log loss: <=64 features; [n_train,iterations1..500,rate in (0,1],depth1..8,min_leaf,task0squared/1binary,seed]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_gradient_boosting([[0.0,1.0],[1.0,3.0],[2.0,5.0],[3.0,7.0],[1.5,0.0]], [4.0,20.0,0.1,2.0,1.0,0.0,42.0])] | | chem_group_folds | scalar | Balanced indivisible group folds: rows [group_id], parameters [n_folds,seed]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_group_folds([[1.0],[1.0],[2.0],[3.0]], [2.0,42.0])] | | chem_haar_transform | scalar | Orthonormal Haar forward/inverse: power-of-two curve rows; [inverse=0,levels=full]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_haar_transform([[1.0,2.0,3.0,4.0]], [])] | | chem_hierarchical_cluster | scalar | Euclidean agglomeration with deterministic tie breaks: <=500 feature rows and bounded cubic work; [clusters,linkage0single/1complete/2average/3Ward=2]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_hierarchical_cluster([[0.0],[1.0],[4.0],[5.0]], [2.0,3.0])] | | chem_holdout_split | scalar | Finite rows, scalar integer class labels if stratified; [train_fraction in(0,1),seed,stratified0/1=0]. Per-group floor clamped to keep train/test nonempty, every stratum>=2; zero-based indices. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_holdout_split([[0.0],[0.0],[1.0],[1.0]], [0.5,42.0,1.0])] | | chem_holt | scalar | Holt linear/damped trend smoothing: >=2 ordered scalar rows; [alpha0..1,beta0..1,horizon1..10000,phi in (0,1]=1]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_holt([[2.0],[4.0],[3.0],[5.0],[7.0],[6.0],[8.0],[9.0]], [0.4,0.3,2.0,0.9])] | | chem_huber_regression | scalar | Fixed-scale Huber M-regression by IRLS: [features...,target]; [delta=1.345,scale=0OLS_MAD,max_iterations=200,tolerance=1e-8]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_huber_regression([[0.0,1.0],[1.0,3.0],[2.0,5.0],[3.0,27.0],[4.0,9.0],[5.0,11.0],[6.0,13.0]], [1.345,1.0])] | | chem_ipw_effect | scalar | Hajek IPW ATE/ATT with fixed-nuisance delta-method SE: [outcome,treatment,propensity]; [estimand0ATE/1ATT=0,explicit_clip=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_ipw_effect([[4.0,1.0,0.5],[6.0,1.0,0.75],[1.0,0.0,0.25],[3.0,0.0,0.5]], [])] | | chem_ir_chart | scalar | Individuals/moving-range chart; ordered scalar rows; optional fixed [center,sigma]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_ir_chart([[1.0],[2.0],[4.0]], [])] | | chem_irt_fit | scalar | Bounded1PL/2PL/3PL marginal item likelihood under fixed N(0,1): item rows,optional last integer frequency; [model=2,freq=0,missing=0,maxiter=300,tol=1e-6,oddq=41,cmax=.5,guess_penalty=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_irt_fit([[0.0,0.0,30.0],[0.0,1.0,20.0],[1.0,0.0,20.0],[1.0,1.0,30.0]], [1.0,1.0,0.0,300.0,1e-08,61.0,0.5,0.0])] | | chem_irt_predict | scalar | Saved IRT characteristic/information curves: scalar ability rows and unchanged model_parameters; D1 logistic probabilities,individual/test Fisher information. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_irt_predict([[0.5],[-1e+300],[1e+300]], [1.0,3.0,1.0,0.0,61.0,2.0,0.5,0.2])] | | chem_irt_score | scalar | Posterior EAP ability scoring: binary item response rows,optional -1missing under saved policy,and unchanged model_parameters; finite extreme scores with quadrature diagnostics. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_irt_score([[0.0,0.0],[0.0,1.0],[1.0,1.0]], [1.0,1.0,2.0,0.0,61.0,1.0,0.0,0.0,1.0,0.0,0.0])] | | chem_item_reliability | scalar | Complete finite respondents/items; [standardized0raw/1correlation=0]. Cronbach alpha,deleted-item alpha,corrected item-total correlations; negative alpha retained,undefined or nonrepresentable quantities null. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_item_reliability([[0.0,0.0,0.0],[1.0,2.0,1.0],[2.0,4.0,0.0],[3.0,6.0,1.0]], [])] | | chem_kalman_smoother | scalar | Known scalar-observation Gaussian Kalman filtering/RTS smoothing: [y,observed0/1] rows; [k,horizon,confidence,m0,P0,F,Q,H,R] row-major; k<=8,n<=5000,h<=500. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_kalman_smoother([[1.0,1.0],[2.0,1.0],[1.5,1.0]], [1.0,2.0,0.95,0.0,1.0,1.0,0.2,1.0,0.5])] | | chem_kaplan_meier | scalar | Kaplan-Meier survival for rows [time,event 0/1], with tied event/censor risk sets. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_kaplan_meier([[1.0,1.0],[2.0,0.0],[3.0,1.0]], []::DOUBLE[])] | | chem_kde_gaussian | scalar | Gaussian kernel density: scalar samples; [bandwidth>0,evaluation_points...], 1..4096 points and <=20000000 kernel terms. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_kde_gaussian([[1.0],[2.0],[3.0]], [0.5,1.0,2.0,3.0])] | | chem_kinship_model | scalar | Gaussian y=Xbeta+g+e with supplied PSD kinship K; rows[y,fixed covariates...,K row]; [p,method0ML/1REML=1,h_upper=.99999999,grid=128,tol=1e-10]. Bounded nonnegative variance components; profile likelihood and BLUP. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_kinship_model([[0.0,1.0,1.0,0.0,0.0,0.0,0.0],[2.0,1.0,1.0,0.0,0.0,0.0,0.0],[4.0,0.0,0.0,1.0,1.0,0.0,0.0],[6.0,0.0,0.0,1.0,1.0,0.0,0.0],[8.0,0.0,0.0,0.0,0.0,1.0,1.0],[10.0,0.0,0.0,0.0,0.0,1.0,1.0]], [0.0,1.0])] | | chem_kinship_predict | scalar | Saved Gaussian genomic model scoring: rows[fixed covariates(p),cross-kinship to n training individuals]; unchanged kinship_model.predict_parameters. Reuses fixed effects and training kernel weights. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_kinship_predict([[1,1,0,0,0,0],[0,0,0,0,0,0]], [1,6,0,0,0.5,10,0,0.9999999999999999,-0.18749999984346133,-0.18749999984346133,0,0,0.18749999984346133,0.18749999984346133])] | | chem_kmeans | scalar | Deterministic k-means: [clusters,max_iterations,seed]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_kmeans([[0.0],[1.0],[9.0],[10.0]], [2.0,100.0,42.0])] | | chem_knn_predict | scalar | K nearest neighbors with training-prefix-only population scaling: [features...,target]; [n_train,k1..n_train,task0regression/1classification]; suffix predictions. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_knn_predict([[0.0,0.0],[1.0,0.0],[3.0,1.0],[4.0,1.0],[2.0,99.0]], [4.0,3.0,1.0])] | | chem_kruskal_wallis | scalar | Tie-corrected Kruskal-Wallis test: [value,group_id]; []. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_kruskal_wallis([[1.0,0.0],[2.0,0.0],[3.0,1.0],[5.0,1.0]], [])] | | chem_laney_p_chart | scalar | Laney P-prime overdispersion limits estimated from standardized moving ranges: [defectives,n]; []. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_laney_p_chart([[1.0,10.0],[9.0,10.0]], [])] | | chem_laney_u_chart | scalar | Laney U-prime overdispersion limits estimated from standardized moving ranges: [count,exposure]; []. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_laney_u_chart([[1.0,1.0],[9.0,1.0]], [])] | | chem_latin_hypercube | scalar | Seeded jittered Latin hypercube: 1..64 [low,high] factor bounds; [runs1..100000,seed]; <=1000000 output cells. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_latin_hypercube([[-1.0,1.0],[0.0,10.0]], [4.0,42.0])] | | chem_levene_test | scalar | Levene/Brown-Forsythe variance test: [value,group_id]; [center1median/0mean]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_levene_test([[1.0,0.0],[2.0,0.0],[4.0,0.0],[2.0,1.0],[4.0,1.0],[7.0,1.0]], [1.0])] | | chem_levey_jennings | scalar | Levey-Jennings fixed three-sigma limits: scalar rows; [reference_mean,reference_sigma]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_levey_jennings([[0.0],[3.1]], [0.0,1.0])] | | chem_lift_curve | scalar | [outcome0/1,finite_score,optional_weight>=0]; []; both classes required. Cumulative selection/gain/response/lift with equal-score batches; lift at zero selection null. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_lift_curve([[0.0,0.1],[1.0,0.4],[0.0,0.4],[1.0,0.8]], [])] | | chem_linear_model | scalar | OLS with coefficient, residual and overall-model inference: [features...,target]; [confidence=.95]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_linear_model([[0.0,1.0],[1.0,2.0],[2.0,4.0],[3.0,4.5]], [0.95])] | | chem_ljung_box | scalar | Ljung-Box autocorrelation diagnostic with chi-square approximation: ordered nonconstant scalar rows; [max_lag1..min(n-1,10000),model_df=0=0,event0/1,group0/1]; []; same-time censors remain at risk. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_logrank_test([[1.0,1.0,0.0],[2.0,0.0,0.0],[3.0,1.0,0.0],[2.0,1.0,1.0],[4.0,0.0,1.0],[5.0,1.0,1.0]], [])] | | chem_mahalanobis | scalar | Squared Mahalanobis distance to training-prefix moments: <=256 features; [n_reference>=2,diagonal_ridge=0]; nonempty query suffix, singular covariance rejected. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_mahalanobis([[-1.0,-1.0],[-1.0,1.0],[1.0,-1.0],[1.0,1.0],[2.0,0.0]], [4.0,0.0])] | | chem_mann_whitney | scalar | Mann-Whitney asymptotic test with ties and continuity correction: [value,group0/1]; []. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_mann_whitney([[1.0,0.0],[2.0,0.0],[3.0,1.0],[4.0,1.0]], [])] | | chem_manova | scalar | Whole-model numeric MANOVA with intercept: [X...,Y...] rows, <=32predictors/responses; [x_columns]; H/E and4criteria, exact rank-one F otherwise JMP approximate F with Roy p lower bound. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_manova([[0.0,-1.0,-1.0],[0.0,-1.0,1.0],[0.0,1.0,-1.0],[0.0,1.0,1.0],[1.0,1.0,0.0],[1.0,1.0,2.0],[1.0,3.0,0.0],[1.0,3.0,2.0]], [1.0])] | | chem_marker_admixture | scalar | Diploid independent-locus binomial admixture EM: [K,maxiter=1000,tolerance=1e-8,seed=0,n_init=3,frequency_floor=1e-8]. Genotypes0/1/2,-1missing. Joint Q/F local likelihood, convergence diagnostic, saved fixed-F inference. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_marker_admixture([[0.0,0.0],[1.0,2.0],[2.0,-1.0]], [1.0,1000.0,1e-9,42.0,1.0])] | | chem_marker_cross | scalar | Complete parental genotype rows0/1/2; [parent0_index,parent1_index,offspring_count,seed]. Single-generation unlinked diploid meiosis, independent loci; <=1Moutputcells. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_marker_cross([[0.0,2.0,1.0],[2.0,0.0,1.0]], [0.0,1.0,3.0,42.0])] | | chem_marker_impute | scalar | Diploid 0/1/2/-1 matrix; [method0mean/1mode/2LDknn/3HWE/4observed/5uniform/6specified=0,nearest_markers=10,nearest_samples=5,seed=0,specified=0]. All-missing loci rejected; mean/mode save values. LD-kNN squared dosage correlation, mean taxicab, majority call; no valid neighbors falls back mode. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_marker_impute([[0.0,0.0],[1.0,1.0],[2.0,2.0],[-1.0,2.0]], [2.0,1.0,1.0])] | | chem_marker_impute_predict | scalar | New dosage rows in [0,2] or missing=-1; saved mean/mode predict_parameters (one per marker). No query-driven fitting. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_marker_impute_predict([[-1.0,-1.0],[2.0,0.0]], [0.5,1.5])] | | chem_marker_relatedness | scalar | Dosages in [0,2] or missing=-1; [method0VanRaden1/1VanRaden2/2diploidDominance=0]. <=1000individuals and100Mpairfeaturework. Additive missing mean-imputed; dominance requires discrete calls and missing centered effect0; monomorphic markers omitted. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_marker_relatedness([[0.0,0.0],[1.0,1.0],[2.0,2.0]], [0.0])] | | chem_marker_simulate | scalar | Scalar rows of allele frequencies in [0,1]; [samples,seed,inbreeding0..1=0]. Independent diploid loci with P(0)=q^2+Fpq,P(1)=2pq(1-F),P(2)=p^2+Fpq; <=1Moutputcells and1024markers. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_marker_simulate([[0.25],[0.5]], [3.0,42.0,0.0])] | | chem_marker_statistics | scalar | Diploid genotype call rows (individuals x markers); 0/1/2,missing=-1; []. Call rates, allele/MAF/heterozygosity/PIC, asymptotic Pearson HWE df1 and BH p-values; monomorphic/all-missing HWE null. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_marker_statistics([[0.0,0.0],[1.0,1.0],[1.0,2.0],[2.0,-1.0]], [])] | | chem_mds | scalar | Classical metric Gower MDS, not optimized/nonmetric Stress1: 2..128 feature or distance rows; [dimensions=min(2,n-1),input0features/1distances=0,standardize0/1=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_mds([[0.0],[1.0],[2.0]], [1.0])] | | chem_mixed_model | scalar | Unbalanced Gaussian random-intercept ML/REML with <=64 fixed features: [features...,y,integer_group]; [method0ML/1REML=1,maxiter1..10000=200,tolerance=1e-8]; bounded variance-ratio search. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_mixed_model([[1.0,0.0],[2.0,0.0],[1.0,0.0],[2.0,0.0],[4.0,1.0],[5.0,1.0],[4.0,1.0],[5.0,1.0],[7.0,2.0],[8.0,2.0],[7.0,2.0],[8.0,2.0]], [1.0])] | | chem_mixed_predict | scalar | Random-intercept prediction: [fixed_features...,integer_group]; saved mixed_model predict_parameters; known-group BLUP and zero-effect new-group predictions. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_mixed_predict([[0.0],[1.0],[99.0]], [0.0,0.0,4.5,0.0,2.0,0.0,-1.0,1.0,1.0])] | | chem_mixture_lattice | scalar | Lower-bound-offset simplex lattice filtered by upper bounds: 1..64 [nonnegative_low,high] rows with low<=high; [degree1..1000,total>0=1]; <=1000000 enumerated compositions. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_mixture_lattice([[0.0,1.0],[0.0,1.0],[0.0,1.0]], [2.0])] | | chem_mlp | scalar | CPU single-hidden-layer tanh Adam network with training-prefix-only scaling: [n_train,hidden1..128,epochs1..10000,rate in (0,1],task0regression/1binary,seed,tolerance=1e-8]; bounded work. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_mlp([[0.0,1.0],[1.0,3.0],[2.0,5.0],[3.0,7.0],[1.5,0.0]], [4.0,4.0,100.0,0.02,0.0,42.0])] | | chem_model_compare | scalar | [observed,prediction1,...] <=64 models; [task0regression/1binary=0,metric0RMSEorLogLoss/1MAEorBrier=0]. Rank caller-supplied predictions, no refitting; binary clipped log loss. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_model_compare([[0.0,0.0,1.0],[1.0,1.0,0.4],[2.0,2.0,1.0]], [])] | | chem_moving_average | scalar | Trailing simple moving average: ordered scalar rows; [window1..n]; no future observations are used. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_moving_average([[2.0],[4.0],[3.0],[5.0],[7.0],[6.0],[8.0],[9.0]], [3.0])] | | chem_moving_average_chart | scalar | Unweighted moving-average control chart, partial startup windows: scalar rows; [window,L,known_center,known_sigma]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_moving_average_chart([[1.0],[2.0],[3.0]], [2.0,3.0,0.0,1.0])] | | chem_multiple_correspondence_analysis | scalar | Complete integer-coded MCA: 2..32categorical variables, <=64observedcategories; [dimensions=min(2,n-1,categories-variables)]; indicator/Burt inertias, principal scores, Benzecri/Greenacre adjustments. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_multiple_correspondence_analysis([[0.0,0.0],[0.0,0.0],[1.0,1.0],[1.0,1.0]], [1.0])] | | chem_nhpp_powerlaw | scalar | Single-system time-truncated power NHPP MLE: scalar exact recurrence times; [observation_end]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_nhpp_powerlaw([[1.0],[2.0],[4.0],[7.0]], [10.0])] | | chem_nhpp_predict | scalar | Power NHPP cumulative/intensity prediction: nonnegative scalar times; [shape,log_eta]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_nhpp_predict([[0.0],[5.0],[10.0]], [2.0,1.6094379124341003])] | | chem_normality_test | scalar | Jarque-Bera skew/kurtosis normality test with asymptotic chi-square p: >=3 nonconstant scalar rows; []; large samples recommended. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_normality_test([[1.0],[2.0],[2.0],[3.0],[4.0],[4.0],[5.0],[7.0]], [])] | | chem_np_chart | scalar | Binomial count-nonconforming control limits, constant sample size: [defectives,n]; []. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_np_chart([[2.0,100.0],[4.0,100.0]], [])] | | chem_odds_ratio | scalar | Log-Wald odds ratio: positive 2x2 integer count table; [confidence=.95,continuity_correction=0]; correction must be explicit for zeros. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_odds_ratio([[20.0,10.0],[10.0,20.0]], [])] | | chem_one_sample_t | scalar | One-sample t inference: scalar rows; [null_mean,confidence=.95]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_one_sample_t([[1.0],[2.0],[4.0]], [0.0,0.95])] | | chem_one_way_anova | scalar | One-way ANOVA: rows [value,group_id]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_one_way_anova([[1.0,0.0],[2.0,0.0],[3.0,0.0],[2.0,1.0],[3.0,1.0],[4.0,1.0]], []::DOUBLE[])] | | chem_orthogonal_screening | scalar | Sylvester-Hadamard main-effect screening: 1..64 [low,high] factor bounds; []; interactions may be aliased, not a general definitive or Plackett-Burman design. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_orthogonal_screening([[-1.0,1.0],[-1.0,1.0],[-1.0,1.0],[-1.0,1.0],[-1.0,1.0]], [])] | | chem_p_chart | scalar | Binomial fraction-nonconforming control limits: [defectives,sample_size]; []. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_p_chart([[2.0,100.0],[4.0,100.0]], [])] | | chem_pacf | scalar | Durbin-Levinson PACF from biased autocovariance: ordered nonconstant scalar rows; [max_lag0..min(n-1,2048)]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_pacf([[2.0],[4.0],[3.0],[5.0],[7.0],[6.0],[8.0],[9.0]], [3.0])] | | chem_paired_t | scalar | Paired t inference: [a,b] rows; [null_difference=0,confidence=.95]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_paired_t([[1.0,0.0],[3.0,1.0],[6.0,3.0]], [])] | | chem_pca | scalar | PCA: parameters [components,standardize 0/1]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_pca([[1.0,2.0],[2.0,4.0],[3.0,6.0]], [1.0,0.0])] | | chem_peak_find | scalar | Sampled local peaks with global prominence: curve rows; [minimum_prominence=0,minimum_distance=1]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_peak_find([[0.0,3.0,1.0,4.0,0.0,2.0,0.0]], [])] | | chem_periodogram | scalar | One-sided unwindowed periodogram density after constant detrending: 2..4096 ordered scalar rows; [sample_interval>0=1]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_periodogram([[2.0],[4.0],[3.0],[5.0],[7.0],[6.0],[8.0],[9.0]], [1.0])] | | chem_permutation_test | scalar | Mean-difference label permutation: [value,group0/1]; [replicates0exact,seed=0,alternative0two/1greater/2less]; bounded exact or Monte Carlo work. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_permutation_test([[0.0,0.0],[1.0,0.0],[2.0,1.0],[3.0,1.0]], [0.0])] | | chem_pls | scalar | PLS1 regression: rows [features...,target], parameters [components]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_pls([[0.0,1.0],[1.0,3.0],[2.0,5.0]], [1.0])] | | chem_pls2_fit | scalar | rows[features...,responses...]; args[n_responses,components,method0NIPALS/1SIMPLS=0,scale_x=1,scale_y=1,confidence=.95]. Centering required; scaling uses training sample SD,constant columns scale1. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_pls2_fit([[-2.0,-3.0,4.0],[-1.0,-1.0,2.0],[0.0,2.0,2.0],[1.0,3.0,1.0],[2.0,6.0,0.0]], [2.0,1.0])] | | chem_pls2_predict | scalar | Saved multivariate PLS prediction, latent scores, conditional-score approximate mean/individual Student intervals; rows[training feature order], exact predict_parameters. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_pls2_predict([[1.0]], [1.0,0.0,1.0,2.0,1.0,5.0,0.95,1.0,-2.0,2.0,1.5811388300841898,-3.0,4.0,4.4,-2.2,3.507135583350036,1.4832396974191324,0.9918365981341756,-0.959403223600247,1.0,4.0,0.26666666666666666,0.23333333333333336])] | | chem_pls2_validate | scalar | rows[features...,responses...,fold_or_role];params[n_responses,max_components,method0NIPALS/1SIMPLS=0,scale_x=1,scale_y=1,mode0supplied_folds/1supplied_roles=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_pls2_validate([[0.0,1.0,0.0],[1.0,3.0,1.0],[2.0,5.0,0.0],[3.0,7.0,1.0]],[1.0,1.0])] | | chem_plsda_fit | scalar | rows[numeric features...,numeric label]; args[components,method=0,scale_x=1,scale_indicator_y=1]. Full one-hot encoding,sorted training labels. Raw predicted indicator scores,largest score class with smallest sorted label on ties; no probability calibration. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_plsda_fit([[-3.0,10.0],[-2.0,10.0],[-1.0,10.0],[1.0,30.0],[2.0,30.0],[3.0,30.0]], [1.0])] | | chem_plsda_predict | scalar | Saved PLS-DA raw indicator scores and largest-score labels; rows[training feature order], exact predict_parameters. Scores are not calibrated probabilities. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_plsda_predict([[-7.0],[0.0],[7.0]], [1.0,1.0,1.0,2.0,1.0,6.0,0.95,0.0,-3.0,3.0,2.3664319132398464,1.0,0.0,-0.5,0.5,0.5477225575051662,0.5477225575051662,-0.9258200997725513,0.9258200997725513,1.0,5.0,0.0,0.0,10.0,30.0])] | | chem_plsda_validate | scalar | rows[features...,numeric_class_label,fold_or_role];params[max_components,method=0,scale_x=1,scale_indicator_y=1,mode=0]. Every training split must contain all evaluation classes. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_plsda_validate([[-3.0,10.0,0.0],[-2.0,10.0,1.0],[-1.0,10.0,0.0],[1.0,30.0,1.0],[2.0,30.0,0.0],[3.0,30.0,1.0]],[1.0])] | | chem_poisson | scalar | Poisson log-link GLM: [features...,count_target]; [lambda=0,max_iterations=100,tolerance=1e-8]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_poisson([[0.0,1.0],[1.0,1.0],[2.0,3.0],[3.0,4.0]], [0.1])] | | chem_polynomial_features | scalar | Total-degree polynomial terms with exponent metadata: features; [degree1..3,include_bias0/1]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_polynomial_features([[1.0,2.0]], [2.0,1.0])] | | chem_pooled_t | scalar | Equal-variance two-sample t: [value,group0/1]; [null_difference=0,confidence=.95]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_pooled_t([[1.0,0.0],[2.0,0.0],[3.0,1.0],[5.0,1.0]], [])] | | chem_power_z | scalar | One-sample known-sigma normal power: [effect,sigma>0]; [n1..1000000000,alpha=.05,alternative0two/1greater/2less]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_power_z([[0.5,1.0]], [20.0,0.05,0.0])] | | chem_precision_recall_curve | scalar | [outcome0/1,finite_score,optional_weight>=0]; []; both classes required. Descending grouped thresholds, initial precision1/recall0; step average_precision and trapezoid_area separately. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_precision_recall_curve([[0.0,0.1],[1.0,0.4],[0.0,0.4],[1.0,0.8]], [])] | | chem_predict | scalar | Linear prediction: saved stable_predict_parameters or [intercept,coefficients...]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_predict([[1.0],[2.0]], [1.0,2.0])] | | chem_process_capability | scalar | Overall sample-SD Pp/Ppk and descriptive mean +/-3SD limits: [LSL,USL,optional within_sigma]. Cp/Cpk require supplied within_sigma. Assumes an appropriate stable normal process. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_process_capability([[1.0],[2.0],[3.0]], [-1.0,5.0])] | | chem_profit_decision | scalar | [outcome0/1,probability0..1,optional_weight>=0]; [profit00,profit01,profit10,profit11], actual rows/decision columns. Max expected-profit binary decision, exact ties choose0; totals outside f64 range null. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_profit_decision([[0.0,0.1],[1.0,0.6],[1.0,0.2]], [2.0,-1.0,-5.0,10.0])] | | chem_propensity_fit | scalar | Training-prefix logistic propensity model: [features...,treatment0/1]; [n_train,ridge=0,max_iterations=100,tolerance=1e-8]. Suffix labels ignored; saved preprocessing. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_propensity_fit([[0.0,0.0],[0.0,0.0],[0.0,0.0],[0.0,1.0],[1.0,0.0],[1.0,1.0],[1.0,1.0],[1.0,1.0]], [8.0])] | | chem_propensity_match | scalar | Greedy1:1 ATT matching with explicit absolute caliper: [outcome,treatment,propensity]; [caliper=1,replacement1/0=1,metric0probability/1logit=0]. Matched-subset effect; SE null. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_propensity_match([[5.0,1.0,0.25],[8.0,1.0,0.75],[9.0,1.0,0.875],[1.0,0.0,0.125],[2.0,0.0,0.375],[4.0,0.0,0.875]], [0.2,1.0,0.0])] | | chem_propensity_predict | scalar | Saved training-only logistic propensity scoring: feature rows; propensity_fit.predict_parameters. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_propensity_predict([[0.0],[1.0]], [1.0,0.0,0.0,0.5,0.5,1.0986122886681098])] | | chem_proportion_test | scalar | Probability-ordered exact binomial test and Wilson interval: binary rows; [null_p,confidence=.95]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_proportion_test([[1.0],[0.0],[1.0],[1.0]], [0.5])] | | chem_quantile_normalize | scalar | Equal-rank across-sample mean quantiles; [] args. Equal input ties receive the mean over tied reference ranks. Saves reference_quantiles. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_quantile_normalize([[5.0,1.0,3.0],[4.0,2.0,6.0]], [])] | | chem_quantile_normalize_predict | scalar | New samples scored against saved sorted reference_quantiles matching row width, with tie averaging; no refitting. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_quantile_normalize_predict([[4.0,4.0,0.0]], [1.5,2.5,5.5])] | | chem_quantile_regression | scalar | Convex check-loss regression with ADMM residual convergence: [features...,target]; [quantile=.5,rho=1,max_iterations=10000,tolerance=1e-7]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_quantile_regression([[0.0,0.0],[1.0,1.0],[2.0,4.0],[3.0,3.0],[4.0,4.0]], [0.5,1.0,20000.0,1e-7])] | | chem_random_forest | scalar | Seeded bootstrap CART forest with feature subsampling: <=64 features; [n_train,trees1..200,depth1..16,min_leaf,task0regression/1classification,seed]; bounded work/model size. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_random_forest([[0.0,1.0],[1.0,3.0],[2.0,5.0],[3.0,7.0],[1.5,0.0]], [4.0,10.0,3.0,1.0,0.0,42.0])] | | chem_random_intercept | scalar | Balanced one-way random-intercept ANOVA variance estimator: rows [value,group_id]. Not a general mixed-effects solver. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_random_intercept([[1.0,0.0],[3.0,0.0],[5.0,1.0],[7.0,1.0]], []::DOUBLE[])] | | chem_random_slope_model | scalar | Correlated Gaussian random slopes: [other_features...,slope,y,subject]; [method=1,maxiter=300,tolerance=1e-8]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_random_slope_model([[-1.5,-0.3999999999999999,0.0],[0.0,2.447801055611894,0.0],[1.5,4.800852965387551,0.0],[-1.43,0.9940080946479155,1.0],[-0.43,2.5060530045266307,1.0],[0.5700000000000001,3.8449574698911446,1.0],[1.57,5.7784240293359295,1.0],[-1.3599999999999999,1.6975969779199587,2.0],[-0.61,2.2441774366691205,2.0],[0.14,2.9939728231014513,2.0],[0.89,4.078268384613996,2.0],[1.6400000000000001,4.508643726327658,2.0],[-1.29,1.3353075168384074,3.0],[0.21000000000000002,1.9584611958404785,3.0],[1.71,3.0969290109247303,3.0],[-1.22,0.7311967164783243,4.0],[-0.21999999999999997,1.053909607842823,4.0],[0.78,2.041094518889131,4.0],[1.78,2.641025660787466,4.0],[-1.15,0.7112645756966788,5.0],[-0.39999999999999997,1.3007179446757398,5.0],[0.35000000000000003,2.4936964602489375,5.0],[1.1,3.0659463709565546,5.0],[1.85,3.871040404872158,5.0],[-1.08,1.113206127673739,6.0],[0.42000000000000004,3.4749909802800594,6.0],[1.92,6.188520632109679,6.0],[-1.01,1.183904059437987,7.0],[-0.25999999999999995,2.6856177920234843,7.0],[0.49000000000000005,4.176074956963551,7.0],[1.24,5.179436461116708,7.0],[1.99,6.850366871258961,7.0]], [])] | | chem_random_slope_predict | scalar | Saved random slope prediction: [other_features...,slope,subject]; exact returned predict_parameters. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_random_slope_predict([[0.0,0.0],[0.5,999.0]], [1,-0.3999999999999999,3.2327332447796517,1.71320244537735,-0.021116948503145182,-1.5,1.7559375000000002,1.134468100518366,0.8414294635617765,8,0,-0.028735991416393714,0.286619733520204,1,0.4445314172359469,0.20056681303699997,2,0.23636025116999793,-0.16587080212282412,3,-0.3765353663119541,-0.43652515986998724,4,-0.6876694303430354,-0.3976689798479615,5,-0.348895922670813,-0.1327743040990701,6,0.3024791285476626,0.27195226638001857,7,0.4584659137885829,0.37370043300261346])] | | chem_randomize_design | scalar | Independent seeded shuffle per replicate then equal-size blocks: finite design rows; [replicates1..1000,seed,blocks=1]; rows divisible by blocks and <=100000 output runs. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_randomize_design([[0.0],[1.0],[2.0],[3.0],[4.0],[5.0]], [2.0,42.0,2.0])] | | chem_rcbd_design | scalar | Randomized complete blocks: distinct treatment rows; [blocks,seed=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_rcbd_design([[0.0],[1.0],[2.0],[3.0]], [3.0,42.0])] | | chem_regression_metrics | scalar | [observed,prediction,optional_weight>=0]; []; <=100000 rows, numeric outcome/prediction absolute values<=1e150. Weighted RMSE/MAE/bias/R2 and residuals; constant-target R2 is null. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_regression_metrics([[1.0,2.0],[2.0,2.0],[3.0,1.0]], [])] | | chem_relative_risk | scalar | Log-Wald relative risk: positive 2x2 integer count table; [confidence=.95,continuity_correction=0]; correction must be explicit for zeros. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_relative_risk([[20.0,10.0],[10.0,20.0]], [])] | | chem_relative_risk_predict | scalar | Centered Cox relative-risk prediction, <=32 features: feature rows; [coefficients(p),feature_origins(p),mean_offsets(p)]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_relative_risk_predict([[0.0],[1.0],[2.0]], [0.6931471805599453,0.0,0.0])] | | chem_reliability_blocks | scalar | Independent reliability blocks: component probability rows; [structure0series/1parallel/2kofn,k_if2]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_reliability_blocks([[0.8,0.7,0.9]], [2.0,2.0])] | | chem_reliability_demonstration | scalar | Independent zero-failure binomial reliability demonstration: target reliability in (0,1) rows; [confidence=.95]; n<=1000000000000. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_reliability_demonstration([[0.95],[0.99]], [0.95])] | | chem_remove_cycle | scalar | Remove one fixed-period cosine/sine cycle: scalar series; [period>2,include_constant=1,n_train=all]. Return amplitude/phase; phase null for numerical zero cycle. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_remove_cycle([[5.0],[6.0],[-1.0],[-2.0],[5.0],[6.0],[-1.0],[-2.0]], [4.0,1.0])] | | chem_remove_linear_trend | scalar | Remove least-squares intercept+slope from equally spaced scalar series: [n_train=all],training-only stable centering/scaling and extrapolated trend. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_remove_linear_trend([[4.0],[4.0],[6.0],[10.0]], [])] | | chem_repeated_covariance | scalar | Nine repeated covariance matrices: scalar increasing times; [structure_code, physical parameters...]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_repeated_covariance([[0.0],[1.0],[2.0]], [4.0,1.0,0.5])] | | chem_repeated_model | scalar | Gaussian repeated measures: [features...,y,subject,time]; [structure,method=1,maxiter=300,tolerance=1e-8,known covariance if method2]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_repeated_model([[0.0,1.1,0.0,0.0],[1.0,1.8,0.0,1.0],[2.0,2.1,0.0,2.0],[0.0,0.8,1.0,0.0],[2.0,2.4,1.0,2.0],[0.0,1.4,2.0,0.0],[1.0,1.9,2.0,1.0],[2.0,2.8,2.0,2.0]], [4.0,2.0,300.0,1e-08,0.7,0.4])] | | chem_report_box_plot_svg | scalar | Type6 box-plot JSON plus SVG: rows[value,group_id,integer_frequency,excluded0/1,hidden0/1,observed0/1,unique_row_id]; [version1,width320..4096,height240..4096,type0Tukey/1quantile,orientation0valueY/1valueX,show_outliers0/1,show_five_number_table0/1]. Closed1.5IQR fences and observed-data whiskers; quantile type full range and extra ticks. Hidden outliers retain provenance but no marks. No Weight,diamonds,notches,jitter; rejects unreadable layout. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_report_box_plot_svg([[1,0,1,0,0,1,0],[2,0,1,0,0,1,1],[3,0,1,0,0,1,2],[4,0,1,0,0,1,3]], [1,900,650,0,0,1,1])] | | chem_report_histogram_svg | scalar | Explicit-edge histogram JSON plus SVG: rows[value,group_id,integer_frequency,excluded0/1,hidden0/1,observed0/1,unique_row_id]; [version1,width320..4096,height240..4096,response0count/1percent/2density,orientation0valueX/1valueY,edge_count,edges...]. Left-closed/right-open bins, final edge included; hidden contributes summaries. Up to16groups,512bins,1e6 total frequency; rejects unreadable layout. No automatic JMP bins or analytic Weight. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_report_histogram_svg([[0,0,1,0,0,1,0],[1,0,1,0,0,1,1],[2,0,1,0,0,1,2]], [1,900,600,0,0,3,0,1,2])] | | chem_resolvable_incomplete_block | scalar | Resolvable affine incomplete blocks: q^2 treatment rows,q=2/3/5/7; [seed=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_resolvable_incomplete_block([[0.0],[1.0],[2.0],[3.0],[4.0],[5.0],[6.0],[7.0],[8.0]], [17.0])] | | chem_response_surface | scalar | Full quadratic response fit: rows [features...,target], parameters [lambda]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_response_surface([[-1.0,2.0],[0.0,1.0],[1.0,4.0]], [0.0])] | | chem_response_surface_predict | scalar | Quadratic prediction using flattened model_parameters from chem_response_surface. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_response_surface_predict([[0.0],[1.0]], [1.0,0.0,1.0,1.0,1.0,2.0])] | | chem_ridge | scalar | Ridge regression: rows [features...,target], parameters [lambda]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_ridge([[0.0,1.0],[1.0,3.0],[2.0,5.0]], [1.0])] | | chem_robust_predict | scalar | Saved Huber/quantile linear prediction with stable origin offsets: feature rows; fit.predict_parameters. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_robust_predict([[2.0],[3.0]], [1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,2.0])] | | chem_roc_curve | scalar | [outcome0/1,finite_score,optional_weight>=0]; []; positive weight in both classes. Descending inclusive thresholds grouped by equal score, trapezoid AUC; first threshold null denotes above maximum. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_roc_curve([[0.0,0.1],[1.0,0.4],[0.0,0.4],[1.0,0.8]], [])] | | chem_rolling_origin | scalar | Chronologically ordered finite rows; [initial_train>=1,horizon>=1,step>=1=1,window0expanding=0]. Full-horizon folds only; training precedes validation, <=1000000 output indices. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_rolling_origin([[0.0],[1.0],[2.0],[3.0],[4.0],[5.0],[6.0],[7.0]], [3.0,2.0,2.0,3.0])] | | chem_sample_rows | scalar | Seeded row sampling: finite matrix; [count1..100000,replacement0/1,seed]; <=1000000 output cells. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_sample_rows([[1.0,2.0],[3.0,4.0],[5.0,6.0]], [2.0,0.0,42.0])] | | chem_sample_size_mean | scalar | Normal-approximation mean margin planning: positive known sigma rows; [margin>0,confidence=.95]; sample size <=1000000000000. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_sample_size_mean([[1.0],[2.0]], [0.5,0.95])] | | chem_sample_size_proportion | scalar | Normal-approximation proportion margin planning: p in [0,1] rows; [margin>0,confidence=.95]; sample size <=1000000000000. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_sample_size_proportion([[0.5],[0.2]], [0.05,0.95])] | | chem_savitzky_golay | scalar | Fixed Savitzky-Golay filter or derivative: equally spaced curve rows; [odd_window,degree=2,derivative=0,spacing=1]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_savitzky_golay([[0.0,1.0,4.0,9.0,16.0,25.0,36.0]], [5.0,2.0,1.0,0.5])] | | chem_scaffold_folds | scalar | Achiral Murcko scaffold grouping and balanced fold assignment. All acyclic molecules share one empty-scaffold group; grouping inherits native chemistry canonicalization limits. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_scaffold_folds(['c1ccccc1','Cc1ccccc1','C1CCCCC1','CCO'], [2.0,42.0])] | | chem_seasonal_decompose | scalar | Classical additive centered-moving-average seasonal decomposition: ordered scalar rows; [period2..floor(n/2)]; unidentified edge terms are null. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_seasonal_decompose([[2.0],[4.0],[3.0],[5.0],[7.0],[6.0],[8.0],[9.0]], [2.0])] | | chem_self_organizing_map | scalar | PCA-initialized rectangular batch local-linear Gaussian SOM, <=64 features and256 nodes: [grid_rows,grid_columns,maxiter=100,bandwidth=0.5,standardize=1,tol=1e-6]; bounded work/model size. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_self_organizing_map([[-1.0],[0.0],[1.0]], [1.0,1.0])] | | chem_sem_fit_indices | scalar | Gaussian SEM fit indices: rows[S_MLrow(p),Sigma row(p),observedmean,modelmean]; [N,kfree]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_sem_fit_indices([[1.0,0.3,1.2,0.2,1.0,1.1],[0.3,1.0,0.2,0.8,-2.0,-2.2]], [120.0,2.0])] | | chem_sem_ram | scalar | Supplied RAM mean/covariance: rows[Brow(p),Omega row(p),intercept]; no parameters. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_sem_ram([[0.0,0.0,0.0,1.0,0.0,0.0,1.0],[0.5,0.0,0.0,0.0,0.8,0.0,2.0],[0.2,0.7,0.0,0.0,0.0,0.6,3.0]], [])] | | chem_simulate_normal | scalar | Box-Muller normal simulation: [mean,sd>=0] rows; [count_per_row,seed]; <=1000000 output samples. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_simulate_normal([[0.0,1.0],[2.0,0.5]], [3.0,42.0])] | | chem_som_predict | scalar | Saved SOM nearest-node predictions on same-width feature rows: unchanged self_organizing_map predict_parameters, <=16384 doubles; distances use fit-preprocessed feature units. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_som_predict([[-2.0],[0.0],[2.0]], [1.0,2.0,0.0,0.0,1.0,-1.0,1.0])] | | chem_spatial_covariance | scalar | Four isotropic JMP spatial kernels: locations in1..3 dimensions; [kind0spherical/1exponential/2Gaussian/3power,partial_sill,nugget,rho]. Independent record nugget; covariance and semivariance matrices. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_spatial_covariance([[0],[1],[0]], [0,4,1,2])] | | chem_spatial_fit | scalar | Profile Gaussian ML/REML spatial regression: rows[coordinates,fixed features,subject,response]; [dimensions1..3,features0..8,kind0..3,reml0/1,nugget0/1,maxiter=1000,tol=1e-8,optional_length_lower,upper]. Estimated sill/range/nugget,interior observed-information Wald intervals and saved prediction; n<=96. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_spatial_fit([[0,0,1],[1,0,2],[2,0,6]], [1,0,0,0,0,100,1e-8,0.1,0.5])] | | chem_spatial_fit_predict | scalar | Reuse estimated spatial fit: rows[coordinates,fixed features,subject]; unchanged fit.predict_parameters. Plug-in universal kriging mean,latent and independent-observation variances; covariance-estimation uncertainty excluded. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_spatial_fit_predict([[10,0]], [1,1,0,3,0,4.666666666666667,0,0.1,0,0,1,1,0,2,2,0,6])] | | chem_spatial_gls | scalar | Gaussian GLS with supplied absolute isotropic spatial covariance; rows[coordinates,fixed features,response]; [dimensions1..3,features0..12,kind,sill,nugget,rho]. Fixed effects,covariance,likelihood and reusable universal-kriging parameters; no covariance estimation. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_spatial_gls([[0,1],[1,2],[2,6]], [1,0,0,2,1,1])] | | chem_spatial_predict | scalar | Saved spatial GLS universal-kriging prediction: rows[coordinates,fixed features]; unchanged spatial_gls.predict_parameters. Mean,latent and independent-observation variances include fixed-effect uncertainty given supplied covariance. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_spatial_predict([[0],[10]], [1,3,1,0,0,2,1,1,3,0,1,2,1,1,1,-2,-1.0000000000000002,3,1,0,1,0,0,1,0.33333333333333337])] | | chem_spatial_variogram | scalar | Empirical isotropic Matheron semivariogram: rows[coordinates1..3,response]; [bins=10,lower=min_pair_distance,upper=max_pair_distance]. Pair counts,mean distance and half mean squared differences; empty classes null. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_spatial_variogram([[0,0],[1,2],[2,4]], [2])] | | chem_spectral_detrend | scalar | Global polynomial baseline removal: curve rows; [degree=1] on a normalized equally spaced grid. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_spectral_detrend([[2.0,3.0,6.0,11.0,18.0]], [2.0])] | | chem_spectral_msc | scalar | MSC reference regression: curve rows; [] fits their mean reference, or args are the saved reference spectrum. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_spectral_msc([[5.0,7.0,11.0,19.0]], [1.0,2.0,4.0,8.0])] | | chem_spectral_snv | scalar | Per-spectrum standard normal variate: curve rows on a common grid; [variance_ddof=1]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_spectral_snv([[1.0,2.0,3.0],[10.0,12.0,14.0]], [])] | | chem_spline_fit | scalar | B-spline regression with second-difference coefficient penalty: [x,y] rows; [degree1..3,lambda,full_clamped_knots...]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_spline_fit([[0.0,1.0],[0.25,1.5],[0.5,2.0],[0.75,2.5],[1.0,3.0]], [1.0,0.0,0.0,0.0,0.5,1.0,1.0])] | | chem_spline_predict | scalar | Saved penalized B-spline prediction: [x] rows; fit.predict_parameters=[degree,knot_count,response_origin,knots...,offset_coefficients...]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_spline_predict([[0.0],[0.5],[1.0]], [1.0,4.0,0.0,0.0,0.0,1.0,1.0,1.0,3.0])] | | chem_split_plot_design | scalar | Classical two-level split plot: factor rows[low,high,role0whole/1subplot]; [replicates=1,seed=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_split_plot_design([[0.0,1.0,0.0],[2.0,3.0,1.0],[4.0,5.0,1.0]], [2.0,31.0])] | | chem_stability_analysis | scalar | Batch linear stability model selection and shelf life: [time,response,batch_id]; [lower_on,lower,upper_on,upper,pooling_alpha,confidence,pool_separate_MSE,horizon]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_stability_analysis([[0.0,10.1,1.0],[1.0,7.9,1.0],[2.0,5.9,1.0],[3.0,4.1,1.0],[0.0,12.1,2.0],[1.0,9.9,2.0],[2.0,7.9,2.0],[3.0,6.1,2.0]], [1.0,3.0,0.0,0.0,0.25,0.95,0.0,20.0])] | | chem_state_space_predict | scalar | Reuse tagged Gaussian or additive-innovations ETS prediction_model: one ignored scalar placeholder row; [horizon,confidence,model...],h<=500. Models preserve final state and uncertainty convention. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_state_space_predict([[0.0]], [2.0,0.95,1.0,0.0,1.0,1.0,0.25,1.0,0.2,1.0,0.5])] | | chem_state_space_smoothing | scalar | Fit 6 additive innovations ETS families: scalar rows; [family0..5,period,horizon,confidence,maxiter,tol]. Estimated initial states,shared innovations ML and forecast intervals; bounded partial ETS. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_state_space_smoothing([[1.0],[2.0],[1.0],[2.0],[1.0],[2.0],[1.0],[2.0]], [0.0,0.0,3.0,0.95,1000.0,1e-08])] | | chem_strip_plot_design | scalar | Classical crossed strip plot: factor rows[low,high,role0row/1column]; [replicates=1,seed=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_strip_plot_design([[0.0,1.0,0.0],[2.0,3.0,1.0],[4.0,5.0,1.0]], [2.0,31.0])] | | chem_summarize | scalar | Column-wise descriptive statistics. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_summarize([[1.0],[2.0],[3.0]], []::DOUBLE[])] | | chem_survival_exponential | scalar | Zero-location exponential lifetime MLE with right censoring: [time>=0,event0/1], positive exposure and >=1 event; []. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_survival_exponential([[1.0,1.0],[2.0,0.0],[3.0,1.0],[5.0,1.0]], [])] | | chem_survival_weibull | scalar | Zero-location Weibull right-censored lifetime MLE: [time>0,event0/1], identifiable positive-event shape; []. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_survival_weibull([[1.0,1.0],[2.0,0.0],[3.0,1.0],[5.0,1.0]], [])] | | chem_svm_fit | scalar | Binary soft-margin C-SVC with dual KKT diagnostics: [features...,label0/1]; [n_train,kernel0linear/1RBF=1,C=1,gamma=1/p,tolerance=1e-6,max_iterations=100000]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_svm_fit([[-2.0,0.0],[-1.0,0.0],[1.0,1.0],[2.0,1.0]], [4.0,0.0,10.0])] | | chem_svm_predict | scalar | Saved binary C-SVC prediction with fixed training preprocessing: feature rows; svm_fit.predict_parameters. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_svm_predict([[-2.0],[2.0]], [1.0,0.0,1.0,0.0,2.0,0.0,0.0,1.0,-0.5,0.5,-1.0,1.0])] | | chem_taguchi_orthogonal_array | scalar | Catalog orthogonal arrays: scalar factor levels2/3; [array4/8/9/18,seed=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_taguchi_orthogonal_array([[2.0],[3.0],[3.0],[3.0],[3.0],[3.0],[3.0],[3.0]], [18.0,11.0])] | | chem_text_dtm | scalar | Aggregate supplied numeric tokens into a document-term count matrix; rows[document_id0based,term_id0based,count]; args[n_documents,n_terms]. Caller tokenizes and defines immutable vocabulary. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_text_dtm([[0,2,1],[0,2,2],[2,0,4]], [4,3])] | | chem_text_latent_class | scalar | Conditional-independence Bernoulli mixture EM on supplied count presence; args[k,seed=0,max_iter=200,tol=1e-8,probability_floor=1e-9]. Reports genuine likelihood,mixture/term probabilities and saved posterior model. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_text_latent_class([[2,1,0],[0,1,3],[0,0,0]], [1,0,200,1e-10,1e-08])] | | chem_text_latent_class_predict | scalar | Score saved Bernoulli mixture using presence and absence of each fitted term; args unchanged text_latent_class.predict_parameters; returns posterior membership and log probability. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_text_latent_class_predict([[1,0],[0,1],[0,0]], [1,2,2,1e-08,0.5,0.5,0.9,0.1,0.1,0.9])] | | chem_text_lsa | scalar | Partial SVD of supplied nonnegative weighted DTM; args[k,preprocess0uncentered/1centered/2centered_scaled=2]; returns U,S,V and scores US with saved transform. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_text_lsa([[3,0],[0,2],[0,0]], [2,0])] | | chem_text_projection | scalar | Score supplied weighted query DTM with saved LSA/topic projection; args unchanged projection_parameters. Training mean,scale,divisor and loadings reused. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_text_projection([[1.5,1.5]], [1,2,2,3,0,0,1,1,1,0,0,1])] | | chem_text_topic_svd | scalar | True orthogonal varimax of supplied-DTM SVD loadings VS; document topic vectors UR preserve reconstruction; args[k,preprocess=2,max_sweeps=200,tol=1e-8,kaiser0/1=1]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_text_topic_svd([[3,0],[0,2],[0,0]], [2,0,200,1e-10,1])] | | chem_text_weight | scalar | Fit corpus dictionary/filter and exact JMP five DTM weightings; nonnegative integer count matrix; args[mode0binary/1ternary/2frequency/3log10/4TFIDF=4,min_corpus_count=1,max_terms=all]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_text_weight([[2,1,0],[0,1,3],[0,0,0]], [4,1,3])] | | chem_text_weight_predict | scalar | Apply saved dictionary and training IDF to new count rows; args unchanged text_weight.weight_parameters; training vocabulary width/order required. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_text_weight_predict([[0,4,2]], [1,3,3,4,3,2,0,1,0.47712125471966244,0.47712125471966244,0.17609125905568124])] | | chem_transfer_function_model | scalar | Single-input rational omega/delta transfer with exact stationary ARMA noise: [X,Y] rows inclfutureX; [n_train,delay,numdegree<=2,dendegree<=2,p<=2,q<=2,h,confidence,maxiter,tol]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_transfer_function_model([[-1.0,1.7],[1.0,2.7],[-1.0,1.3],[1.0,2.3],[-1.0,1.7],[1.0,2.7],[-1.0,1.3],[1.0,2.3],[-1.0,1.7],[1.0,2.7],[-1.0,1.3],[1.0,2.3],[-1.0,1.7],[1.0,2.7],[-1.0,1.3],[1.0,2.3],[-1.0,1.7],[1.0,2.7],[-1.0,1.3],[1.0,2.3],[2.0,0.0],[-2.0,0.0]], [20.0,0.0,0.0,0.0,0.0,0.0,2.0,0.95,100.0,1e-08])] | | chem_type1_gage | scalar | Type1 Gage Cg/Cgk with20% tolerance fraction: scalar measurements; [reference,tolerance_width]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_type1_gage([[9.0],[10.0],[11.0]], [10.0,6.0])] | | chem_u_chart | scalar | Poisson rate control chart: [count,exposure]; []. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_u_chart([[2.0,2.0],[6.0,2.0]], [])] | | chem_uplift_curve | scalar | Supplied-score uplift/Qini evaluation: [factual_outcome,treatment,predicted_effect_score,assignment_propensity]; []. Grouped-score HT gain; no uplift-tree fitting. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_uplift_curve([[3.0,1.0,0.9,0.5],[1.0,0.0,0.8,0.5],[2.0,1.0,0.8,0.5],[2.0,0.0,0.1,0.5]], [])] | | chem_variance_ratio_test | scalar | Normal-model F variance-ratio test: [value,group0/1]; [null_ratio=1,confidence=.95]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_variance_ratio_test([[1.0,0.0],[2.0,0.0],[3.0,1.0],[5.0,1.0]], [])] | | chem_variance_test | scalar | Normal-model variance test and interval: scalar rows; [null_sd,confidence=.95]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_variance_test([[1.0],[2.0],[4.0]], [1.0])] | | chem_welch_t_test | scalar | Two-sided Welch t-test: rows [value,group 0/1]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_welch_t_test([[1.0,0.0],[2.0,0.0],[3.0,0.0],[2.0,1.0],[3.0,1.0],[4.0,1.0]], []::DOUBLE[])] | | chem_western_electric | scalar | Four Western Electric rules on fixed reference limits: scalar rows; [center,sigma]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_western_electric([[2.1],[2.2],[0.0]], [0.0,1.0])] | | chem_wilcoxon_signed_rank | scalar | Paired Wilcoxon signed-rank normal approximation with zero/tie correction: [a,b]; []. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_wilcoxon_signed_rank([[2.0,1.0],[4.0,2.0],[6.0,3.0]], [])] | | chem_xbar_r_chart | scalar | Xbar-R chart using d2/d3 constants, equal subgroups of size2..10; []. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_xbar_r_chart([[1.0,3.0],[2.0,4.0],[3.0,5.0]], [])] | | chem_xbar_s_chart | scalar | Xbar-S chart with c4 correction; equal subgroups of size2..1000; []. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_xbar_s_chart([[1.0,3.0],[2.0,4.0],[3.0,5.0]], [])] | | chem_zi_beta_binomial | scalar | Shared-inflation beta-binomial logistic ML with intraclass delta: [features...,integer response,trials>1,integer frequency]; [maxiter=600,tol=1e-9,confidence=.95,covariance_kind=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_zi_beta_binomial([[0.0,4.0,7212.0],[1.0,4.0,2992.0],[2.0,4.0,2652.0],[3.0,4.0,1872.0],[4.0,4.0,897.0]],[])] | | chem_zi_binomial | scalar | Shared-inflation binomial logistic ML: [features...,integer response,trials,integer frequency]; [maxiter=600,tol=1e-9,confidence=.95,covariance0observed/1HC0=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_zi_binomial([[0.0,4.0,39072.0],[1.0,4.0,24192.0],[2.0,4.0,24192.0],[3.0,4.0,10752.0],[4.0,4.0,1792.0]],[])] | | chem_zi_gamma | scalar | Shared-inflation Gamma log-mean ML,shape=mu/sigma and scale=sigma: [features...,nonnegative response,trials1,integer frequency]; [maxiter=600,tol=1e-9,confidence=.95,covariance_kind=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_zi_gamma([[0,1,3],[1,1,2],[2,1,3],[3,1,2]],[])] | | chem_zi_negative_binomial | scalar | Shared-inflation negative-binomial NB2 log-mean ML: [features...,integer count,trials1,integer frequency]; [maxiter=600,tol=1e-9,confidence=.95,covariance_kind=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_zi_negative_binomial([[0,1,47500],[1,1,17500],[2,1,13125],[3,1,8750],[4,1,5469],[5,1,3281],[6,1,1914],[7,1,1094],[8,1,615],[9,1,342],[10,1,188],[11,1,103],[12,1,56],[13,1,30],[14,1,16],[15,1,9],[16,1,5],[17,1,2],[18,1,1],[19,1,1]],[])] | | chem_zi_poisson | scalar | Shared-inflation Poisson log-mean ML: [features...,integer count,trials1,integer frequency]; [maxiter=600,tol=1e-9,confidence=.95,covariance_kind=0]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_zi_poisson([[0.0,1.0,10.0],[1.0,1.0,3.0],[2.0,1.0,4.0],[3.0,1.0,2.0],[4.0,1.0,1.0]],[])] | | chem_zi_predict | scalar | Saved zero-inflation distribution prediction: [features...,trials,query response,quantile probability]; exact zi-fit.predict_parameters carrier. Joint selected-covariance mean delta Wald inference only for interior identified fits. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_zi_predict([[1.,1.,.5]],[1.0,2.0,0.0,0.95,0.0,0.0,0.0,0.6931471805599453,0.0,0.3,0.0,0.0,0.0,0.0])] | | cosine_bit | scalar | Calculate cosine similarity between equal-length BLOB bit fingerprints. | NULL | [cosine_bit(morgan_fp_bits('CCO'), morgan_fp_bits('CCN'))] | | dice_bit | scalar | Calculate Dice similarity between equal-length BLOB bit fingerprints. | NULL | [dice_bit(morgan_fp_bits('CCO'), morgan_fp_bits('CCN'))] | | dock | scalar | Dock a flexible ligand against a PDB receptor at pH 7.4; return scored poses as JSON using box half-extents in angstroms. | NULL | [dock('CCO', 'ATOM 1 CA ALA A 1 0.000 0.000 0.000 1.00 20.00 C', 0.0, 0.0, 0.0, 3.0, 3.0, 3.0, 1, 42)] | | dock | scalar | Dock a flexible ligand against a PDB receptor at the requested pH; return scored poses as JSON using box half-extents in angstroms. | NULL | [dock('CCO', 'ATOM 1 CA ALA A 1 0.000 0.000 0.000 1.00 20.00 C', 0.0, 0.0, 0.0, 3.0, 3.0, 3.0, 1, 42, 7.4)] | | druglikeness_pass | scalar | Return 1 or 0 for a named drug-likeness rule panel, or NULL for an unsupported rule or input. | NULL | [druglikeness_pass('CCO', 'lipinski')] | | enrichment_factor | scalar | Calculate active enrichment in the top fraction of a screen ranked by ascending score. | NULL | [enrichment_factor([-3.0, -2.0, -1.0, 0.0], [true, false, true, false], 0.5)] | | formula_composition_json | scalar | Export sorted element/isotope counts as JSON. Repeated tokens are summed; labelled and unlabelled atoms remain distinct. NULL for invalid formula input. | NULL | [formula_composition_json('[13C]CH4')] | | formula_dbe | scalar | Calculate conservative neutral closed-shell DBE for CHNOFClBrI formulas, including isotopes. NULL for unsupported elements, negative or half-integer DBE, or invalid syntax. Does not validate molecular identity. | NULL | [formula_dbe('C8H10N4O2')] | | formula_exact_mass | scalar | Sum neutral monoisotopic atomic masses for a strict flat formula using the versioned AFIX 18-symbol table. NULL for unsupported or malformed input. | NULL | [formula_exact_mass('C8H10N4O2')] | | formula_monoisotopic_mass | scalar | Sum neutral atomic masses from a strict flat formula, including explicit isotope labels such as [13C] and [2H], using the pinned SMILES periodic table. Unlabelled elements use their most abundant isotope. NULL for unknown isotopes, unsupported syntax or invalid input. | NULL | [formula_monoisotopic_mass('[13C]6H12O6')] | | formula_weight | scalar | Calculate formula weight using pinned average atomic weights for unlabelled atoms and explicit isotope masses for labelled atoms. NULL for invalid input. | NULL | [formula_weight('C2H6O')] | | fraction_csp3 | scalar | Return the fraction of carbon atoms classified as sp3. | NULL | [fraction_csp3('CCO')] | | fragment_parent | scalar | Normalize, remove salts, select the largest fragment and neutralize supported charges. | NULL | [fragment_parent('CCO')] | | generic_scaffold | scalar | Extract a scaffold with carbon atoms and single bonds as SMILES. | NULL | [generic_scaffold('Cc1ccccc1')] | | inchi_charge | scalar | Extract the InChI charge layer (q), or an empty string when absent. | NULL | [inchi_charge('InChI=1S/C2H4O2/c1-2(3)4/h1H3,(H,3,4)')] | | inchi_connections | scalar | Extract the InChI connectivity layer (c), or an empty string when absent. | NULL | [inchi_connections('InChI=1S/C2H4O2/c1-2(3)4/h1H3,(H,3,4)')] | | inchi_formula | scalar | Extract the molecular formula layer from an InChI. | NULL | [inchi_formula('InChI=1S/C2H4O2/c1-2(3)4/h1H3,(H,3,4)')] | | inchi_has_stereo | scalar | Test whether an InChI contains bond or tetrahedral stereo layers. | NULL | [inchi_has_stereo('InChI=1S/C2H4O2/c1-2(3)4/h1H3,(H,3,4)')] | | inchi_hydrogens | scalar | Extract the InChI hydrogen layer (h), or an empty string when absent. | NULL | [inchi_hydrogens('InChI=1S/C2H4O2/c1-2(3)4/h1H3,(H,3,4)')] | | inchi_is_standard | scalar | Test whether an InChI has the standard 1S version marker. | NULL | [inchi_is_standard('InChI=1S/C2H4O2/c1-2(3)4/h1H3,(H,3,4)')] | | inchi_is_valid | scalar | Test whether text has a supported InChI syntax. | NULL | [inchi_is_valid('InChI=1S/C2H4O2/c1-2(3)4/h1H3,(H,3,4)')] | | inchi_num_stereo_centers | scalar | Count entries in the InChI tetrahedral stereo layer. | NULL | [inchi_num_stereo_centers('InChI=1S/C2H4O2/c1-2(3)4/h1H3,(H,3,4)')] | | inchi_skeleton_match | scalar | Compare InChI formula, connectivity and hydrogen layers while ignoring stereo layers. | NULL | [inchi_skeleton_match('InChI=1S/C2H4O2/c1-2(3)4/h1H3,(H,3,4)', 'InChI=1S/C2H4O2/c1-2(3)4/h1H3,(H,3,4)')] | | inchi_stereo_bond | scalar | Extract the InChI double-bond stereo layer (b), or an empty string when absent. | NULL | [inchi_stereo_bond('InChI=1S/C2H4O2/c1-2(3)4/h1H3,(H,3,4)')] | | inchi_stereo_tetrahedral | scalar | Extract the InChI tetrahedral stereo layer (t), or an empty string when absent. | NULL | [inchi_stereo_tetrahedral('InChI=1S/C2H4O2/c1-2(3)4/h1H3,(H,3,4)')] | | inchi_version | scalar | Extract the InChI version marker, including S for standard InChI. | NULL | [inchi_version('InChI=1S/C2H4O2/c1-2(3)4/h1H3,(H,3,4)')] | | inchikey_connectivity | scalar | Extract the first 14-character connectivity block of an InChIKey. | NULL | [inchikey_connectivity('QTBSBXVTEAMEQO-UHFFFAOYSA-N')] | | inchikey_is_valid | scalar | Test the uppercase 14-10-1 character format of an InChIKey. | NULL | [inchikey_is_valid('QTBSBXVTEAMEQO-UHFFFAOYSA-N')] | | inchikey_protonation | scalar | Extract the final protonation character of an InChIKey. | NULL | [inchikey_protonation('QTBSBXVTEAMEQO-UHFFFAOYSA-N')] | | inchikey_stereo | scalar | Extract the second 10-character block of an InChIKey. | NULL | [inchikey_stereo('QTBSBXVTEAMEQO-UHFFFAOYSA-N')] | | kulczynski_bit | scalar | Calculate Kulczynski similarity between equal-length BLOB bit fingerprints. | NULL | [kulczynski_bit(morgan_fp_bits('CCO'), morgan_fp_bits('CCN'))] | | largest_fragment | scalar | Keep the largest connected fragment and return its SMILES. | NULL | [largest_fragment('CCO')] | | lipinski_violations | scalar | Count violations of the Lipinski rule-of-five thresholds. | NULL | [lipinski_violations('CCO')] | | logp_crippen | scalar | Estimate octanol/water logP using Wildman-Crippen atom contributions. | NULL | [logp_crippen('CCO')] | | maccs_keys | scalar | Return the 166 MACCS structural keys in a 21-byte bit fingerprint. | NULL | [maccs_keys('CCO')] | | mass_error_ppm | scalar | Calculate signed mass error as (observed_mass-reference_mass)/reference_mass*1e6. NULL for nonpositive or nonfinite masses and nonfinite output. | NULL | [mass_error_ppm(100.001, 100.0)] | | mcconnaughey_bit | scalar | Calculate McConnaughey similarity between equal-length BLOB bit fingerprints. | NULL | [mcconnaughey_bit(morgan_fp_bits('CCO'), morgan_fp_bits('CCN'))] | | mcs_json | scalar | Return a bounded maximum common substructure result and atom mappings as JSON. | NULL | [mcs_json('CCO', 'CCN')] | | mcs_smarts | scalar | Return a bounded maximum common substructure search result as SMARTS. | NULL | [mcs_smarts('CCO', 'CCN')] | | mol_aromatic_fraction | scalar | Return the fraction of heavy atoms classified as aromatic; zero when no heavy atoms exist. | NULL | [mol_aromatic_fraction('c1ccccc1O')] | | mol_block_atoms_json | scalar | Return atoms and coordinates from the first MOL block as JSON. | NULL | [mol_block_atoms_json('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | mol_block_bonds_json | scalar | Return bonds and 1-based atom indices from the first MOL block as JSON. | NULL | [mol_block_bonds_json('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | mol_block_centroid_x | scalar | Return the mean x coordinate of atoms in the first MOL block. | NULL | [mol_block_centroid_x('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | mol_block_centroid_y | scalar | Return the mean y coordinate of atoms in the first MOL block. | NULL | [mol_block_centroid_y('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | mol_block_centroid_z | scalar | Return the mean z coordinate of atoms in the first MOL block. | NULL | [mol_block_centroid_z('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | mol_block_formula | scalar | Return the formula from the atoms recorded in the first MOL block. | NULL | [mol_block_formula('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | mol_block_has_3d | scalar | Test whether any atom in the first MOL block has an absolute z coordinate above 0.0001. | NULL | [mol_block_has_3d('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | mol_block_json | scalar | Return the first MOL record, geometry and properties as JSON. | NULL | [mol_block_json('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | mol_block_max_x | scalar | Return the maximum x coordinate of atoms in the first MOL block. | NULL | [mol_block_max_x('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | mol_block_max_y | scalar | Return the maximum y coordinate of atoms in the first MOL block. | NULL | [mol_block_max_y('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | mol_block_max_z | scalar | Return the maximum z coordinate of atoms in the first MOL block. | NULL | [mol_block_max_z('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | mol_block_min_x | scalar | Return the minimum x coordinate of atoms in the first MOL block. | NULL | [mol_block_min_x('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | mol_block_min_y | scalar | Return the minimum y coordinate of atoms in the first MOL block. | NULL | [mol_block_min_y('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | mol_block_min_z | scalar | Return the minimum z coordinate of atoms in the first MOL block. | NULL | [mol_block_min_z('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | mol_block_name | scalar | Extract the name of the first MOL block. | NULL | [mol_block_name('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | mol_block_num_atoms | scalar | Count atoms recorded in the first MOL block. | NULL | [mol_block_num_atoms('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | mol_block_num_bonds | scalar | Count bonds recorded in the first MOL block. | NULL | [mol_block_num_bonds('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | mol_block_properties_json | scalar | Return the first MOL/SDF record properties as JSON. | NULL | [mol_block_properties_json('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | mol_block_property | scalar | Extract a named property from the first MOL/SDF record, or NULL when absent. | NULL | [mol_block_property('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
', 'ID')] | | mol_block_radius_of_gyration | scalar | Calculate the unweighted radius of gyration of coordinates in the first MOL block. | NULL | [mol_block_radius_of_gyration('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | mol_block_weight | scalar | Calculate molecular weight from the atoms recorded in the first MOL block. | NULL | [mol_block_weight('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | mol_exact_mass | scalar | Calculate monoisotopic molecular mass from SMILES. | NULL | [mol_exact_mass('CCO')] | | mol_formal_charge | scalar | Sum formal charges on all atoms in the molecular graph. | NULL | [mol_formal_charge('CCO')] | | mol_formula | scalar | Return the molecular formula in Hill order, including implicit hydrogens. | NULL | [mol_formula('CCO')] | | mol_graph_json | scalar | Export a versioned native atom/bond graph sidecar in input parse order, preserving explicit H vertices, isotope, charge and raw stereo tokens. Not pretrained ms-pred tensors or RDKit sanitization. NULL for invalid, unsupported or resource-limited input. | NULL | [mol_graph_json('N[C@@H](C)C(=O)O')] | | mol_has_substructure | scalar | Test whether a molecule matches a supported SMARTS pattern. | NULL | [mol_has_substructure('CCO', 'CO')] | | mol_hash | scalar | Return a molecular grouping hash for a supported method; list methods with mol_hash_methods(). | NULL | [mol_hash('CCO', 'element_graph')] | | mol_hash_methods | scalar | List supported molecular hash method names as JSON. | NULL | [mol_hash_methods()] | | mol_heavy_atom_mass | scalar | Sum isotope-aware heavy-atom masses, using average weights for unlabelled atoms and excluding all hydrogen isotopes. | NULL | [mol_heavy_atom_mass('CCO')] | | mol_heteroatom_fraction | scalar | Return the fraction of heavy atoms that are neither carbon nor hydrogen; zero when no heavy atoms exist. | NULL | [mol_heteroatom_fraction('CCO')] | | mol_is_valid | scalar | Test whether the SMILES is accepted by the supported parser. | NULL | [mol_is_valid('CCO')] | | mol_largest_ring_size | scalar | Return the largest ring size in the perceived symmetrized SSSR, or zero for acyclic molecules. | NULL | [mol_largest_ring_size('c1ccccc1O')] | | mol_mean_degree | scalar | Return twice the heavy-heavy bond count divided by the heavy-atom count; zero when no heavy atoms exist. | NULL | [mol_mean_degree('CCO')] | | mol_mr | scalar | Estimate molar refractivity using Wildman-Crippen atom contributions. | NULL | [mol_mr('CCO')] | | mol_num_aromatic_atoms | scalar | Count atoms classified as aromatic by native perception. | NULL | [mol_num_aromatic_atoms('c1ccccc1O')] | | mol_num_aromatic_bonds | scalar | Count bonds classified as aromatic by native perception. | NULL | [mol_num_aromatic_bonds('c1ccccc1O')] | | mol_num_atoms | scalar | Count non-hydrogen atoms in a SMILES molecule. | NULL | [mol_num_atoms('CCO')] | | mol_num_bonds | scalar | Count bonds in a SMILES molecule, with each bond counted once regardless of order. | NULL | [mol_num_bonds('CCO')] | | mol_num_carbons | scalar | Count carbon atoms in the molecular graph. | NULL | [mol_num_carbons('CCO')] | | mol_num_double_bonds | scalar | Count double bonds in the molecular graph. | NULL | [mol_num_double_bonds('CCO')] | | mol_num_explicit_h | scalar | Count hydrogen vertices and explicit hydrogen annotations on non-hydrogen atoms. | NULL | [mol_num_explicit_h('CCO')] | | mol_num_fragments | scalar | Count connected components in the molecular graph. | NULL | [mol_num_fragments('CCO')] | | mol_num_halogens | scalar | Count fluorine, chlorine, bromine and iodine atoms. | NULL | [mol_num_halogens('CCO')] | | mol_num_implicit_h | scalar | Count implicit hydrogens on non-hydrogen atoms after native aromaticity perception. | NULL | [mol_num_implicit_h('CCO')] | | mol_num_nitrogens | scalar | Count nitrogen atoms in the molecular graph. | NULL | [mol_num_nitrogens('CCO')] | | mol_num_oxygens | scalar | Count oxygen atoms in the molecular graph. | NULL | [mol_num_oxygens('CCO')] | | mol_num_ring_atoms | scalar | Count atoms belonging to at least one perceived ring. | NULL | [mol_num_ring_atoms('c1ccccc1O')] | | mol_num_ring_bonds | scalar | Count bonds belonging to at least one perceived ring. | NULL | [mol_num_ring_bonds('c1ccccc1O')] | | mol_num_single_bonds | scalar | Count single bonds, including bonds to explicit hydrogen atoms. | NULL | [mol_num_single_bonds('CCO')] | | mol_num_total_h | scalar | Count explicit and implicit hydrogens in the molecular graph. | NULL | [mol_num_total_h('CCO')] | | mol_num_triple_bonds | scalar | Count triple bonds in the molecular graph. | NULL | [mol_num_triple_bonds('CCO')] | | mol_substructure_count | scalar | Count unique atom-set matches for a supported SMARTS pattern. | NULL | [mol_substructure_count('CCO', 'CO')] | | mol_substructure_matches_json | scalar | Return unique SMARTS atom-set matches as 1-based atom indices in JSON. | NULL | [mol_substructure_matches_json('CCO', 'CO')] | | mol_weight | scalar | Calculate molecular weight from standard atomic weights, including implicit hydrogens. | NULL | [mol_weight('CCO')] | | morgan_fp_bits | scalar | Return a native Morgan bit fingerprint with radius 2 and 2048 bits. | NULL | [morgan_fp_bits('CCO')] | | morgan_fp_bits | scalar | Return a native Morgan bit fingerprint with the requested radius and bit count. | NULL | [morgan_fp_bits('CCO', 2, 2048)] | | murcko_scaffold | scalar | Extract the Bemis-Murcko ring-and-linker scaffold as SMILES. | NULL | [murcko_scaffold('Cc1ccccc1')] | | neutralize_charges | scalar | Neutralize supported charged atom patterns and return SMILES. | NULL | [neutralize_charges('CCO')] | | normalize_smiles | scalar | Normalize supported functional-group patterns and return SMILES. | NULL | [normalize_smiles('CCO')] | | num_aliphatic_carbocycles | scalar | Count non-aromatic rings containing only carbon atoms. | NULL | [num_aliphatic_carbocycles('Cc1ccccc1')] | | num_aliphatic_heterocycles | scalar | Count non-aromatic rings containing a non-carbon atom. | NULL | [num_aliphatic_heterocycles('Cc1ccccc1')] | | num_aliphatic_rings | scalar | Count rings that are not fully aromatic. | NULL | [num_aliphatic_rings('Cc1ccccc1')] | | num_aromatic_carbocycles | scalar | Count aromatic rings containing only carbon atoms. | NULL | [num_aromatic_carbocycles('Cc1ccccc1')] | | num_aromatic_heterocycles | scalar | Count aromatic rings containing a non-carbon atom. | NULL | [num_aromatic_heterocycles('Cc1ccccc1')] | | num_aromatic_rings | scalar | Count fully aromatic rings in the perceived ring set. | NULL | [num_aromatic_rings('Cc1ccccc1')] | | num_h_acceptors | scalar | Count hydrogen-bond acceptors using the native atom rules. | NULL | [num_h_acceptors('CCO')] | | num_h_donors | scalar | Count hydrogen-bond donor atoms using the native atom rules. | NULL | [num_h_donors('CCO')] | | num_heteroatoms | scalar | Count atoms other than carbon and hydrogen. | NULL | [num_heteroatoms('CCO')] | | num_rotatable_bonds | scalar | Count rotatable bonds using the native strict bond rules. | NULL | [num_rotatable_bonds('CCO')] | | num_saturated_carbocycles | scalar | Count single-bond rings containing only carbon atoms. | NULL | [num_saturated_carbocycles('Cc1ccccc1')] | | num_saturated_heterocycles | scalar | Count single-bond rings containing a non-carbon atom. | NULL | [num_saturated_heterocycles('Cc1ccccc1')] | | num_saturated_rings | scalar | Count rings containing only single bonds. | NULL | [num_saturated_rings('Cc1ccccc1')] | | pdb_to_pdbqt | scalar | Convert protein PDB text to PDBQT with native atom typing. | NULL | [pdb_to_pdbqt('ATOM 1 CA ALA A 1 0.000 0.000 0.000 1.00 20.00 C')] | | prepare_receptor | scalar | Prepare a PDB receptor with native pH-dependent protonation and polar hydrogens, returning PDBQT. | NULL | [prepare_receptor('ATOM 1 CA ALA A 1 0.000 0.000 0.000 1.00 20.00 C', 7.4)] | | qed | scalar | Calculate the weighted QED drug-likeness score using native descriptors. | NULL | [qed('CCO')] | | ring_count | scalar | Count rings in the native perceived ring set. | NULL | [ring_count('Cc1ccccc1')] | | ring_systems_json | scalar | Return ring systems and their 1-based atom and bond indices as JSON. | NULL | [ring_systems_json('Cc1ccccc1')] | | roc_auc | scalar | Calculate ROC AUC for screening scores where lower is better and true labels mark actives. | NULL | [roc_auc([-3.0, -2.0, -1.0, 0.0], [true, false, true, false])] | | russel_bit | scalar | Calculate Russel similarity between equal-length BLOB bit fingerprints. | NULL | [russel_bit(morgan_fp_bits('CCO'), morgan_fp_bits('CCN'))] | | scaffold_network_json | scalar | Return a bounded ring-removal scaffold network as JSON. | NULL | [scaffold_network_json('Cc1ccccc1')] | | sdf_count | scalar | Count parsed molecule records in SDF text. | NULL | [sdf_count('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | sdf_properties_json | scalar | Return each parsed SDF record name and properties as JSON. | NULL | [sdf_properties_json('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
')] | | sdf_property | scalar | Extract a named SDF property using a 1-based record index, or NULL when absent. | NULL | [sdf_property('water
ducksmiles

1 0 0 0 0 0 0 0 0 0999 V2000
0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
M END
>
water

$$$$
', 1, 'ID')] | | selfies_is_valid | scalar | Test whether a SELFIES string can be decoded by the supported decoder. | NULL | [selfies_is_valid('[C][C][O]')] | | selfies_to_smiles | scalar | Decode supported SELFIES tokens to SMILES. | NULL | [selfies_to_smiles('[C][C][O]')] | | smiles_to_pdbqt | scalar | Generate a seeded 3D ligand conformer from SMILES and return PDBQT. | NULL | [smiles_to_pdbqt('CCO', 42)] | | smiles_to_selfies | scalar | Encode supported SMILES as SELFIES. | NULL | [smiles_to_selfies('CCO')] | | sokal_bit | scalar | Calculate Sokal similarity between equal-length BLOB bit fingerprints. | NULL | [sokal_bit(morgan_fp_bits('CCO'), morgan_fp_bits('CCN'))] | | strip_salts | scalar | Remove recognized salt fragments and return the remaining SMILES. | NULL | [strip_salts('CCO')] | | structural_alert_count | scalar | Count matched toxicophore structural alerts. | NULL | [structural_alert_count('CCO')] | | structural_alerts_json | scalar | Return matched toxicophore structural alerts as JSON. | NULL | [structural_alerts_json('CCO')] | | structure_atom_count | scalar | Count atoms in auto-detected PDB, mmCIF or XYZ text. | NULL | [structure_atom_count('ATOM 1 CA ALA A 1 0.000 0.000 0.000 1.00 20.00 C')] | | structure_centroid_x | scalar | Return the mean x coordinate of atoms in auto-detected PDB, mmCIF or XYZ text. | NULL | [structure_centroid_x('ATOM 1 CA ALA A 1 0.000 0.000 0.000 1.00 20.00 C')] | | structure_centroid_y | scalar | Return the mean y coordinate of atoms in auto-detected PDB, mmCIF or XYZ text. | NULL | [structure_centroid_y('ATOM 1 CA ALA A 1 0.000 0.000 0.000 1.00 20.00 C')] | | structure_centroid_z | scalar | Return the mean z coordinate of atoms in auto-detected PDB, mmCIF or XYZ text. | NULL | [structure_centroid_z('ATOM 1 CA ALA A 1 0.000 0.000 0.000 1.00 20.00 C')] | | structure_chain_count | scalar | Count distinct chains in auto-detected PDB, mmCIF or XYZ text. | NULL | [structure_chain_count('ATOM 1 CA ALA A 1 0.000 0.000 0.000 1.00 20.00 C')] | | structure_max_x | scalar | Return the maximum x coordinate of atoms in auto-detected PDB, mmCIF or XYZ text. | NULL | [structure_max_x('ATOM 1 CA ALA A 1 0.000 0.000 0.000 1.00 20.00 C')] | | structure_max_y | scalar | Return the maximum y coordinate of atoms in auto-detected PDB, mmCIF or XYZ text. | NULL | [structure_max_y('ATOM 1 CA ALA A 1 0.000 0.000 0.000 1.00 20.00 C')] | | structure_max_z | scalar | Return the maximum z coordinate of atoms in auto-detected PDB, mmCIF or XYZ text. | NULL | [structure_max_z('ATOM 1 CA ALA A 1 0.000 0.000 0.000 1.00 20.00 C')] | | structure_min_x | scalar | Return the minimum x coordinate of atoms in auto-detected PDB, mmCIF or XYZ text. | NULL | [structure_min_x('ATOM 1 CA ALA A 1 0.000 0.000 0.000 1.00 20.00 C')] | | structure_min_y | scalar | Return the minimum y coordinate of atoms in auto-detected PDB, mmCIF or XYZ text. | NULL | [structure_min_y('ATOM 1 CA ALA A 1 0.000 0.000 0.000 1.00 20.00 C')] | | structure_min_z | scalar | Return the minimum z coordinate of atoms in auto-detected PDB, mmCIF or XYZ text. | NULL | [structure_min_z('ATOM 1 CA ALA A 1 0.000 0.000 0.000 1.00 20.00 C')] | | structure_model_count | scalar | Count models in auto-detected PDB, mmCIF or XYZ text. | NULL | [structure_model_count('ATOM 1 CA ALA A 1 0.000 0.000 0.000 1.00 20.00 C')] | | structure_radius_of_gyration | scalar | Calculate the unweighted radius of gyration of a PDB, mmCIF or XYZ structure. | NULL | [structure_radius_of_gyration('ATOM 1 CA ALA A 1 0.000 0.000 0.000 1.00 20.00 C')] | | structure_residue_count | scalar | Count distinct residues in auto-detected PDB, mmCIF or XYZ text. | NULL | [structure_residue_count('ATOM 1 CA ALA A 1 0.000 0.000 0.000 1.00 20.00 C')] | | tanimoto_bit | scalar | Calculate Tanimoto similarity between equal-length BLOB bit fingerprints. | NULL | [tanimoto_bit(morgan_fp_bits('CCO'), morgan_fp_bits('CCN'))] | | tpsa | scalar | Calculate topological polar surface area from nitrogen and oxygen atom contributions. | NULL | [tpsa('CCO')] | | tversky_bit | scalar | Calculate Tversky similarity between equal-length BLOB bit fingerprints with alpha and beta weights. | NULL | [tversky_bit(morgan_fp_bits('CCO'), morgan_fp_bits('CCN'), 0.5, 0.5)] | ### Overloaded Functions
This extension does not add any function overloads. ### Added Types
This extension does not add any types. ### Added Settings
| name | description | input_type | scope | aliases | typed_value | |----------------|---------------------------------------------------------|------------|--------|---------|-------------| | temp_directory | Set the directory to which temporary files are written. | VARCHAR | GLOBAL | [] | post.db.tmp |