{
  "name": "NextStat",
  "tagline": "The rigorous inference engine a Claude Science agent calls when a number has to be defensible.",
  "url": "https://nextstat.io",
  "docs": "https://nextstat.io/docs/claude-science",
  "updated": "2026-07-01",
  "install": "pip install nextstat",
  "license": "AGPL-3.0-or-later OR LicenseRef-Commercial",
  "connector": {
    "protocol": "Model Context Protocol (MCP)",
    "discover": "nextstat.tools.get_mcp_tools()",
    "invoke": "nextstat.tools.handle_mcp_call(name, args)",
    "serverSchema": "GET /v1/tools/schema",
    "serverExecute": "POST /v1/tools/execute",
    "toolCount": 31
  },
  "moat": [
    "7-tier numerical parity contract — 1e-12 per-bin vs pyhf / ROOT",
    "Signed validation_report (JSON + PDF) with dataset SHA-256, model spec and environment",
    "Reproducible DOI / Zenodo benchmark snapshots and replication bundles",
    "21 CFR Part 11 and ICH M15 regulatory reporting surfaces (IQ/OQ/PQ)",
    "Deterministic execution mode — bit-identical results the reviewer can re-run"
  ],
  "verify_statistic": {
    "tool": "verify_statistic",
    "summary": "Recompute any statistic an LLM claimed (hazard ratio, p-value, ATE, CLs limit, PopPK parameter) from NextStat's validated Rust core and compare within the parity tolerance.",
    "verdicts": [
      "PASS",
      "FAIL",
      "RECOMPUTED"
    ],
    "evidence": "signed validation_report (JSON + PDF)"
  },
  "capabilities": [
    {
      "group": "Clinical & survival",
      "vertical": "Survival analysis",
      "tool": "nextstat_survival_fit",
      "inputs": "duration, event, covariates (Arrow / Parquet / CSV)",
      "artifact": "hazard ratios, SE, Schoenfeld PH test + signed validation_report",
      "verify": "recompute HR / CI and the PH-test p-value under the parity contract"
    },
    {
      "group": "Clinical & survival",
      "vertical": "Kaplan–Meier / log-rank",
      "tool": "nextstat_kaplan_meier",
      "inputs": "duration, event, group",
      "artifact": "survival curve, median survival, log-rank p-value",
      "verify": "independently recompute the log-rank statistic"
    },
    {
      "group": "Clinical & survival",
      "vertical": "Competing risks",
      "tool": "nextstat_competing_risks",
      "inputs": "duration, event-type, covariates",
      "artifact": "Aalen–Johansen CIF, Gray's test, Fine–Gray HR",
      "verify": "recompute the CIF and Gray's test"
    },
    {
      "group": "Pharmacometrics (PK/PD)",
      "vertical": "Population PK (NLME)",
      "tool": "nextstat_pharma_fit",
      "inputs": "CDISC .xpt or dosing + concentration records",
      "artifact": "FOCE / FOCEI / SAEM θ, Ω, Σ with RSE% and NONMEM-parity report",
      "verify": "re-estimate with fixed seed; structural parity vs the NONMEM reference"
    },
    {
      "group": "Pharmacometrics (PK/PD)",
      "vertical": "PK model diagnostics",
      "tool": "nextstat_pharma_vpc",
      "inputs": "fitted model + observations",
      "artifact": "VPC, NPDE and goodness-of-fit diagnostics",
      "verify": "regenerate the VPC prediction intervals"
    },
    {
      "group": "Pharmacometrics (PK/PD)",
      "vertical": "Dose–response",
      "tool": "nextstat_dose_response",
      "inputs": "dose, response",
      "artifact": "Emax / Sigmoid-Emax parameters and ED50",
      "verify": "recompute ED50 and its confidence interval"
    },
    {
      "group": "Pharmacometrics (PK/PD)",
      "vertical": "Bioequivalence",
      "tool": "nextstat_bioequivalence",
      "inputs": "PK exposures (AUC, Cmax) by treatment",
      "artifact": "ABE (TOST) / RSABE 90% CI and power",
      "verify": "recompute the 90% CI and the TOST decision"
    },
    {
      "group": "Regression & Bayesian",
      "vertical": "Generalized linear models",
      "tool": "nextstat_glm_fit",
      "inputs": "formula / design matrix + response",
      "artifact": "coefficients, robust SE, deviance",
      "verify": "recompute coefficients and cluster / robust SE"
    },
    {
      "group": "Regression & Bayesian",
      "vertical": "Bayesian posterior",
      "tool": "nextstat_bayesian_sample",
      "inputs": "log-density model + data",
      "artifact": "NUTS posterior, ESS, R̂, divergences (ArviZ)",
      "verify": "re-sample and check the R̂ / ESS health gates"
    },
    {
      "group": "Econometrics & causal",
      "vertical": "Panel fixed effects",
      "tool": "nextstat_panel_fe",
      "inputs": "panel (entity, time, y, X)",
      "artifact": "within estimator + cluster-robust SE",
      "verify": "recompute the FE point estimate and SE"
    },
    {
      "group": "Econometrics & causal",
      "vertical": "Difference-in-Differences",
      "tool": "nextstat_did",
      "inputs": "treatment, period, outcome",
      "artifact": "TWFE ATT + cluster-robust SE",
      "verify": "recompute the ATT independently"
    },
    {
      "group": "Econometrics & causal",
      "vertical": "Doubly-robust ATE/ATT",
      "tool": "nextstat_aipw",
      "inputs": "treatment, outcome, covariates",
      "artifact": "AIPW ATE / ATT, propensity diagnostics, E-value",
      "verify": "recompute the ATE and the E-value sensitivity bound"
    },
    {
      "group": "Particle physics (HEP)",
      "vertical": "Hypothesis test (CLs)",
      "tool": "nextstat_hypotest",
      "inputs": "pyhf / HS3 workspace JSON, μ",
      "artifact": "asymptotic CLs at μ, parity-gated vs pyhf / ROOT",
      "verify": "recompute q_μ within 1e-12 per-bin parity"
    },
    {
      "group": "Particle physics (HEP)",
      "vertical": "Upper limit",
      "tool": "nextstat_upper_limit",
      "inputs": "workspace JSON",
      "artifact": "95% CL upper limit via CLs scan",
      "verify": "re-scan the CLs curve"
    },
    {
      "group": "Particle physics (HEP)",
      "vertical": "Discovery significance",
      "tool": "nextstat_discovery_asymptotic",
      "inputs": "workspace JSON",
      "artifact": "Z₀ from the background-only test",
      "verify": "recompute Z₀ / p₀"
    },
    {
      "group": "Time series, risk & reliability",
      "vertical": "Meta-analysis",
      "tool": "nextstat_meta_analysis",
      "inputs": "effect sizes + variances",
      "artifact": "fixed / random effects, I², τ², Q",
      "verify": "recompute the pooled effect and heterogeneity"
    },
    {
      "group": "Time series, risk & reliability",
      "vertical": "Volatility",
      "tool": "nextstat_garch_fit",
      "inputs": "return series",
      "artifact": "GARCH / EGARCH / GJR parameters + conditional volatility",
      "verify": "recompute the log-likelihood and parameters"
    },
    {
      "group": "Time series, risk & reliability",
      "vertical": "Rare-event reliability",
      "tool": "nextstat_fault_tree_ce_is",
      "inputs": "fault tree + basic-event probabilities",
      "artifact": "top-event probability (p ~ 1e-16, CE-IS)",
      "verify": "re-estimate with the importance-sampling seed"
    }
  ]
}