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ducksmiles
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Extension descriptor (YAML)

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

Maintainer(s): nkwork9999

Compatibility Matrix

This table covers all DuckDB releases that have not reached end-of-life, 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 

INSTALL ducksmiles FROM community;
LOAD ducksmiles;

Example 

-- 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');

About ducksmiles

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

418 SQL overloads, including the existing 151 molecular overloads and 267 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:

  • 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. 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).

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<lag]. Returns versioned JSON; NULL for invalid, nonfinite, rank-deficient or resource-limited inputs. See ANALYTICS.md for parameter order and method limits. | NULL | [chem_ljung_box([[2.0],[4.0],[3.0],[5.0],[7.0],[6.0],[8.0],[9.0]], [3.0,0.0])] | | chem_loess | scalar | Tricube local polynomial smoothing with optional robust bisquare weights: [x,y] rows; [span,degree0..2=1,robust_iterations0..10=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_loess([[0.0,1.0],[1.0,3.0],[2.0,5.0],[3.0,7.0],[4.0,9.0]], [1.0,1.0,0.0])] | | chem_loess_predict | scalar | Saved LOESS neighborhood prediction: [x] rows; fit.predict_parameters=[span,degree,n_train,x_train…,y_train…,robust_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_loess_predict([[0.5],[2.5]], [1.0,1.0,5.0,0.0,1.0,2.0,3.0,4.0,1.0,3.0,5.0,7.0,9.0,1.0,1.0,1.0,1.0,1.0])] | | chem_logistic | scalar | Binomial logit GLM, standardized penalty: [features…,binary_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_logistic([[0.0,0.0],[1.0,1.0],[2.0,0.0],[3.0,1.0]], [1.0])] | | chem_logrank_test | scalar | Two-group right-censored log-rank chi-square test: [time>=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_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]], [])] | | 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_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')] | | 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')] | | 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('NC@@HC(=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