{ "_id": "6a580d6bff0138dec5d7254b", "Package": "gdpar", "Type": "Package", "Title": "General Dynamic Parameter Models via Reference Anchoring", "Version": "0.1.0", "Authors@R": "person(\"José Mauricio\", \"Gómez Julián\",\nemail = \"isadore.nabi@pm.me\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0009-0000-2412-3150\"))", "Description": "Implements a unified predictive framework in which\nindividual parameters are decomposed as theta_i equal to\ntheta_ref plus Delta(x_i, theta_ref), with theta_ref a\npopulation reference and Delta an explicit deviation function.\nThe decomposition follows the Additive-Multiplicative-Modulated\ncanonical form and is estimated through three complementary\npaths: hierarchical Bayesian inference via 'Stan',\nvarying-coefficient models via penalized splines, and amortized\ninference via hypernetworks in 'torch'. The package provides\nidentifiability diagnostics, validity tests for the population\nreference, and benchmarks against canonical zero-inflated count\ndatasets and avian abundance data from the eBird Status and\nTrends project. The framework and its estimation paths are\ndescribed in Gomez Julian (2026) .", "License": "GPL (>= 3)", "Encoding": "UTF-8", "URL": "https://github.com/IsadoreNabi/gdpar", "BugReports": "https://github.com/IsadoreNabi/gdpar/issues", "Additional_repositories": "https://stan-dev.r-universe.dev,\nhttps://inla.r-inla-download.org/R/stable", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "RoxygenNote": "8.0.0", "NeedsCompilation": "no", "Packaged": { "Date": "2026-07-15 21:43:53 UTC", "User": "root" }, "Author": "José Mauricio Gómez Julián [aut, cre] (ORCID:\n)", "Maintainer": "José Mauricio Gómez Julián ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-07-15 18:10:02 UTC", "RemoteUrl": "https://github.com/cran/gdpar", "RemoteRef": "HEAD", "RemoteSha": "2be5392367992d694e7afaac59d51024b9ab59bd", "_user": "cran", "_type": "src", "_file": "gdpar_0.1.0.tar.gz", "_fileid": 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gdpar_prior objects", "topics": [ "print.gdpar_prior" ] }, { "page": "print.gdpar_spatial_dependence_diagnostic", "title": "Print method for gdpar_spatial_dependence_diagnostic objects", "topics": [ "print.gdpar_spatial_dependence_diagnostic" ] }, { "page": "print.gdpar_spatial_dependence_robust", "title": "Print method for gdpar_spatial_dependence_robust objects", "topics": [ "print.gdpar_spatial_dependence_robust" ] }, { "page": "print.summary.gdpar_causal_bridge", "title": "Print method for summary.gdpar_causal_bridge objects", "topics": [ "print.summary.gdpar_causal_bridge" ] }, { "page": "print.summary.gdpar_eb_fb_comparison", "title": "Print method for summary.gdpar_eb_fb_comparison", "topics": [ "print.summary.gdpar_eb_fb_comparison" ] }, { "page": "print.summary.gdpar_eb_fit", "title": "Print method for summary.gdpar_eb_fit", "topics": [ "print.summary.gdpar_eb_fit" ] }, { "page": "print.summary.gdpar_meta_learner_comparison", "title": "Print method for summary.gdpar_meta_learner_comparison objects", "topics": [ "print.summary.gdpar_meta_learner_comparison" ] }, { "page": "print.W_basis", "title": "Print method for W_basis objects", "topics": [ "print.W_basis" ] }, { "page": "residuals.gdpar_fit", "title": "Residuals for a fitted gdpar model", "topics": [ "residuals.gdpar_fit" ] }, { "page": "summary.gdpar_causal_bridge", "title": "Summary method for gdpar_causal_bridge objects", "topics": [ "summary.gdpar_causal_bridge" ] }, { "page": "summary.gdpar_coef", "title": "Summary method for gdpar_coef objects", "topics": [ "summary.gdpar_coef" ] }, { "page": "summary.gdpar_eb_fb_comparison", "title": "Summary method for gdpar_eb_fb_comparison", "topics": [ "summary.gdpar_eb_fb_comparison" ] }, { "page": "summary.gdpar_eb_fit", "title": "Summary method for gdpar_eb_fit", "topics": [ "summary.gdpar_eb_fit" ] }, { "page": "summary.gdpar_fit", "title": "Summary method for gdpar_fit objects", "topics": [ "summary.gdpar_fit" ] }, { "page": "summary.gdpar_ksd_joint", "title": "Summary method for gdpar_ksd_joint", "topics": [ "summary.gdpar_ksd_joint" ] }, { "page": "summary.gdpar_meta_learner_comparison", "title": "Summary method for gdpar_meta_learner_comparison objects", "topics": [ "summary.gdpar_meta_learner_comparison" ] }, { "page": "summary.gdpar_preflight_report", "title": "Summary method for gdpar_preflight_report", "topics": [ "summary.gdpar_preflight_report" ] }, { "page": "W_basis", "title": "Construct a functional basis for the modulating component W", "topics": [ "W_basis" ] } ], "_readme": "https://github.com/cran/gdpar/raw/HEAD/README.md", "_rundeps": [ "abind", "backports", "checkmate", "cli", "distributional", "generics", "glue", "lifecycle", "magrittr", "matrixStats", "numDeriv", "pillar", "pkgconfig", "posterior", "rlang", "tensorA", "tibble", "utf8", "vctrs", "withr" ], "_vignettes": [ { "source": "v08d_amm_subphases.Rmd", "filename": "v08d_amm_subphases.html", "title": "Theoretical Addendum -- Block 8 (Sub-phases 8.3.1--8.3.10):", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. Purpose and Relation to the v08 Family*", "2. Setting and Notation", "2.1. AMM with $K \\geq 1$ Distributional Slots", "2.2. Slot Conventions", "3. Sub-phase 8.3.1 -- param_specs as the Per-Slot Resolution Layer", "4. Sub-phase 8.3.2 -- The (D-ID) Per-Slot Identifiability Check (4C)", "5. Sub-phase 8.3.4 -- Custom Families and lognormal_loc_scale", "6. Sub-phase 8.3.5a -- Student-$t$ ($K = 3$, stan_id = 8)", "7. Sub-phase 8.3.5b -- Tweedie ($K = 3$, stan_id = 9)", "7.1. Hybrid lpdf: Dunn-Smyth series + saddlepoint", "7.2. THETA_REF_PRIOR_BLOCK per-family expansion", "7.3. Initial-value helper", "8. Sub-phase 8.3.6 -- Mixtures and Hurdle Models", "8.1. Algebraic decompositions", "8.2. Structural decisions", "8.3. Deferred cases", "9. Sub-phase 8.3.7 -- Heterogeneous Families per Slot", "10. Sub-phase 8.3.8 -- B-spline W Basis (D-D3)", "10.1. Cox-de Boor recurrence (D2 detailed)", "10.2. Boundary knot semantics", "11. Sub-phase 8.3.9 -- Residuals G1 / G2 / G3 + S3 Methods Re-validation + Route B predict.gdpar_fit for $K > 1$", "11.1. (G1) Pearson-type residual per slot", "11.2. (G2) Randomized quantile residual (Dunn-Smyth) per slot", "11.3. (G3) DHARMa-compatible scaled residual per slot", "11.4. S3 method re-validation under $K > 1$", "11.5. Bootstrapped goldens and the 16-column manifest", "12. Sub-phase 8.3.10 -- Release-Gate for Session 8.3", "12.1. Fuzz testing protocol", "12.2. Debts recorded at release-gate", "13. Synthesis: The Per-Slot Architecture as a Coherent Design", "14. Implementation Status (Release-Gate Context)", "15. References (selective)" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "vop02_arbitrary_p.Rmd", "filename": "vop02_arbitrary_p.html", "title": "Arbitrary p: Operational Cookbook for Multivariate Fits", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. What this vignette covers", "2. Building a multivariate spec", "2.1. Direct construction with dimwise()", "2.2. Per-dimension overrides with override()", "2.3. Chainable construction with amm_build()", "2.4. Canonical serialisation: amm_save_spec() and amm_load_spec()", "3. Multivariate families: gdpar_family_multi()", "3.1. Construction", "3.2. Auto-promotion", "3.3. Constraints", "4. The modulating component for p > 1", "4.1. Materialisation at a given p", "4.2. Splitting a separable basis with as_per_k()", "4.3. Why W stays top-level", "5. Calling gdpar() for p > 1", "5.1. Outcome layout", "5.2. Parametrization aggregation", "5.3. Full call signature for the multivariate path", "6. Inspecting the pre-flight report", "7. Extracting predictions and coefficients", "7.1. predict() returns a 3-D array", "7.2. coef() returns a unified gdpar_coef object", "7.3. as.data.frame() for tidy pipelines", "8. End-to-end worked example", "9. Reporting parametrization decisions across scenarios", "9.1. Reading the CSV directly", "9.2. Hit-rate aggregation", "9.3. Faceted plot", "10. PSIS-LOO via gdpar_loo()", "10.1. Default aggregation: per-subject", "10.2. Diagnostic aggregation: per-cell", "10.3. Pareto-$k$ caveats", "10.4. Experimental status", "11. Known limitations", "11.1. Separable W only", "11.2. Per-coordinate heterogeneous families are a future block (per-slot heterogeneity is implemented as of 8.3.7)", "11.3. Single global sigma_W shared across blocks", "11.4. Multi-parametric extension is a future block", "11.5. Pre-flight wall-time", "12. References and cross-references" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "v04_asymptotics_path1_bayesian.Rmd", "filename": "v04_asymptotics_path1_bayesian.html", "title": "Theoretical Addendum -- Block 4:", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. Purpose", "2. Setting and Notation", "2.1. The Hierarchical Bayesian Path 1 Model", "2.1.1. Two design regimes: random and conditional", "2.1.2. Generative assumption on the data", "2.2. Distance on the Parameter Space", "2.3. Notation Summary", "3. Three Asymptotic Layers", "4. Standing Asymptotic Hypotheses", "5. Theorem 4A: Posterior Consistency", "6. Theorem 4B: Posterior Contraction Rate", "6.1. Specialization to AMM Levels", "6.2. Numerical Verification of Contraction", "7. Theorem 4C: Bernstein-von Mises", "7.1. Semiparametric Bernstein-von Mises (Partial Result)", "8. Specialization to AMM Special Cases (Block 3)", "9. Open Questions", "10. Implementation Implications for Path 1 (Stan / cmdstanr)", "10.1. Prior Specification and (PRIOR-KL), (PRIOR-THICK)", "10.2. Convergence Diagnostics and (TEST), (SIEVE)", "10.3. Bernstein-von Mises Calibration Check", "10.4. (REG-EST) of Block 2 Specialized to Path 1", "11. Summary", "12. Connections to Subsequent Blocks", "Appendix A. Asymptotic Notation", "Appendix B. Asymptotic Hypothesis Table", "References Cited in This Block" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "v05_asymptotics_path2_vcm.Rmd", "filename": "v05_asymptotics_path2_vcm.html", "title": "Theoretical Addendum -- Block 5:", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. Purpose", "2. Setting and Notation", "2.1. The Path 2 Model", "2.1.1. Two design regimes (parallel to Block 4)", "2.2. Vector vs. Scalar Convention for $\\beta(\\cdot)$", "2.3. Distance on the Parameter Space", "2.3. Notation Summary", "3. Three Asymptotic Layers", "4. Standing Asymptotic Hypotheses for Path 2", "5. Theorem 5A: Pointwise Consistency", "6. Theorem 5B: Uniform Consistency and Rate", "7. Theorem 5C: Pointwise Asymptotic Normality", "7.1. Scope of (L3): Explicit and Tight", "8. Proposition 5D: Adaptive Smoothing under Data-Driven $\\widehat{\\lambda}$", "9. Specialization to Block 3 Special Cases", "10. Open Questions", "11. Implementation Implications for Path 2 (mgcv)", "11.1. Smoothing Parameter Selection", "11.2. Confidence Interval Reporting", "11.3. (REG-EST) of Block 2 Specialized to Path 2", "12. Summary", "13. Connections to Subsequent Blocks", "Appendix A. Asymptotic Notation for Path 2", "Appendix B. Asymptotic Hypothesis Table for Path 2", "References Cited in This Block" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "v06_asymptotics_path3_hypernetwork.Rmd", "filename": "v06_asymptotics_path3_hypernetwork.html", "title": "Theoretical Addendum -- Block 6:", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. Purpose", "2. Setting and Notation", "2.1. The Path 3 Model", "2.1.1. Two design regimes (parallel to Blocks 4-5)", "2.2. Regimes of Asymptotic Analysis", "2.3. Distance and Notation", "3. Three Asymptotic Layers and Their Status for Path 3", "4. Standing Asymptotic Hypotheses for Path 3", "5. Theorem 6A: Consistency under the NTK Regime (Partial)", "6. Theorem 6B: PAC-Bayes Generalization Bound (Non-Asymptotic)", "7. Proposition 6C: Infinite-Width Gaussian Process Limit (Characterization of the Prior)", "8. Proposition 6D: Function-Level Consistency under Fixed Architecture (Partial)", "9. Specialization to Block 3 Special Cases", "10. Open Questions", "11. Implementation Implications for Path 3 (torch)", "11.1. Prior Specification (Bayesian Variant)", "11.2. PAC-Bayes Bound Reporting (Theorem 6B)", "11.3. NTK Diagnostic", "11.4. Discrimination Protocol Cross-Reference", "11.5. (REG-EST) of Block 2 Specialized to Path 3", "12. Summary", "13. Connections to Subsequent Blocks", "Appendix A. Asymptotic Notation for Path 3", "Appendix B. Asymptotic Hypothesis Table for Path 3", "References Cited in This Block" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "v09_cognitive_motivation.Rmd", "filename": "v09_cognitive_motivation.html", "title": "Theoretical Addendum -- Block 9:", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. Purpose", "2. The Cognitive Starting Point: Driver Prediction Formalized", "3. Hierarchical Bayesian Cognition (Tenenbaum, Kemp, Griffiths, Goodman 2011)", "4. Core Knowledge as Structured Prior (Spelke and Kinzler 2007)", "5. The \"Machines That Learn and Think Like People\" Program (Lake, Ullman, Tenenbaum, Gershman 2017)", "6. Probabilistic Program Induction and One-Shot Generalization (Lake, Salakhutdinov, Tenenbaum 2015)", "7. Resource-Rationality (Lieder and Griffiths 2020)", "8. Mapping Cognitive-Science Strands to AMM Features", "9. What the Cognitive Analogy Does and Does Not Justify", "10. Open Questions on Cognitive Grounding", "11. Connections to the Rest of the Addendum", "12. Summary", "References Cited in This Block" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "vop06_meta_learner_comparison.Rmd", "filename": "vop06_meta_learner_comparison.html", "title": "Comparing the AMM-side T-learner against External Meta-learners", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. What this vignette covers", "2. Setup", "3. The grf adapter in three lines", "4. The EconML adapter", "4.1. One-time installation", "4.2. Running the EconML adapter", "4.3. Caveat: serialization of the EconML state", "5. Reading the output", "6. Writing your own adapter (DoubleML as an example)", "7. Troubleshooting (Python-side)", "8. Where to go next" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "vop09_dependence_robust.Rmd", "filename": "vop09_dependence_robust.html", "title": "Dependence-Robust Inference in gdpar (Axis 2)", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. What this vignette covers", "2. The temporal pair (recap)", "3. The spatial pair", "3.1. Diagnostic: Moran's I", "3.2. Robust SE: spatial block bootstrap", "3.3. The default block size, and why n^(1/4)", "3.4. Data-driven block size (block_size = \"auto\")", "4. The full-Bayes path", "5. Reading the result, and the caveats", "References" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "vop05_distributional_K_dharma.Rmd", "filename": "vop05_distributional_K_dharma.html", "title": "Distributional Regression K > 1 and Residual Diagnostics with DHARMa", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. What this vignette covers", "2. The K > 1 API", "2.1. Three equivalent input forms", "2.2. Choosing K", "2.3. End-to-end example: Gaussian K = 2", "2.4. Custom K > 1 families via gdpar_family_custom_K()", "2.5. Prediction", "3. Residual diagnostics: G1 / G2 / G3", "3.1. API", "3.2. Posterior predictive draws and PPC", "4. DHARMa integration (optional)", "4.1. API", "4.2. When to use DHARMa vs the built-in G2", "5. Worked example: zero-inflated negative binomial (K = 3)", "6. Custom family registry: gdpar_family_custom() (K = 1)", "7. Known limitations and future work", "References" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "v07b_eb_multivariate.Rmd", "filename": "v07b_eb_multivariate.html", "title": "Theoretical Addendum -- Block 7 (Multivariate Extension):", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. Purpose and Relation to v07", "1.1. Epistemic Posture and the Critical Audit of v07 §5", "1.2. Reading Order", "2. Notation (Multivariate Extension)", "2.1. Multivariate Population Reference and Heterogeneous Slots", "2.2. Marginal Likelihood in $\\mathbb{R}^p$ and per-Slot Tensor under $K > 1$", "2.3. Sensitivity Jacobian of the Conditional Posterior", "2.4. Total Variation, Weak Metric, and Posterior Distance", "2.5. Notation Summary", "3. Standing Hypotheses (Multivariate Extension)", "3.1. (EB-MARG-ID)$_p$ -- Multivariate Marginal Identifiability", "3.2. (PRIOR-FB-WEAK)$_p$ -- Weak FB Prior in $\\mathbb{R}^p$", "3.3. (HIER-COMPLEX)$_p$ -- Multivariate Hierarchical Regularity", "3.4. Discussion: Why the Asterisked Hypotheses are Strictly Stronger", "4. Theorem 7A* -- First-Order Asymptotic Equivalence (Multivariate)", "4.1. Proof Sketch (Extension of v07's Reference Argument)", "4.2. Why Total Variation is Restricted to Regime A (Multivariate Refinement)", "4.3. Practical Implication of Theorem 7A*", "4.4. Reduction to v07's Theorem 7A under $(p, K) = (1, 1)$", "4.5. Audit Trail: v07 §5 Reviewed Line by Line", "5. Proposition 7B* -- Higher-Order Coverage Discrepancy (Matrix Form)", "5.1. Derivation Sketch", "5.2. Reduction to v07's Scalar Proposition 7B under $p = \\dim(\\xi) = 1$", "5.3. Effective Sensitivity Diagonalization (Practical Form)", "5.4. Operational Note and the eb_correction Argument", "6. Theorem 7C* -- Compound Decision Bound (Multi-Slot $K > 1$)", "6.1. Reduction to v07's Theorem 7C under $K = 1$", "6.2. Two Regimes of Practical Use (Multi-Slot Refinement)", "6.3. Caveat (Squared-Error Loss; Coverage Picture Unchanged)", "6.4. Reference", "7. Proposition 7D* -- Substantial Discrepancy Conditions (Multivariate)", "7.1. Reduction to v07's Proposition 7D under $(p, K) = (1, 1)$", "8. Recommendation by Scenario (Multivariate Extension)", "9. Open Questions", "9.1. (O1*-EBFB) Adaptive Choice between EB and FB Based on Data (Multivariate)", "9.2. (O2*-EBFB) Higher-Order Coverage Correction for Multivariate EB", "9.3. (O3*-EBFB) Behavior under Model Misspecification (Multivariate)", "9.4. (O4*-EBFB) Computational Competitiveness of FB at Moderate $n$ under $K \\cdot p$ Large", "9.5. (O5*-EBFB) Numerical Anti-Fragility of Multivariate Laplace under Non-Conjugacy (New)", "10. Connections to Subsequent Blocks", "11. Joint Kernel Stein Discrepancy: the gdpar_ksd_joint() Helper", "11.1. The Stein Operator and the Stein Kernel", "11.2. Empirical Gaussian Target (B9.4 iteration) and Future Extension", "11.3. Base Kernel and Bandwidth", "11.4. ESS-Weighted Variant", "11.5. Operational Reading: gdpar_compare_eb_fb and gdpar_ksd_joint are Complementary", "12. Summary", "Appendix A. Notation Correspondence: Multivariate vs.\\ Scalar", "Appendix B. Hypothesis Table (Multivariate)", "References Cited in This Block" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "v07_eb_vs_fb.Rmd", "filename": "v07_eb_vs_fb.html", "title": "Theoretical Addendum -- Block 7:", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. Purpose", "2. Setting and Notation", "2.1. Formal Specification of EB and FB", "2.2. Distance Between EB and FB Posteriors", "2.3. Notation Summary", "3. Three Layers of EB-vs-FB Comparison", "4. Standing Hypotheses for EB-vs-FB Comparison", "5. Theorem 7A: First-Order Asymptotic Equivalence of EB and FB", "6. Proposition 7B: Higher-Order Coverage Discrepancy of EB", "7. Theorem 7C: Finite-Sample Compound Decision Bound", "8. Proposition 7D: Conditions under which EB and FB Differ Substantially", "9. When to Use EB vs FB: Recommendation by Scenario", "10. Open Questions", "11. Implementation Implications for Path 1", "11.1. Path 1: Stan Configuration", "11.2. Path 2: Penalty Parameter Selection (EB-like on a Different Object)", "11.3. Path 3: Variational Inference vs. Posterior Sampling", "11.4. (REG-EST) of Block 2 under EB and FB", "12. Summary", "13. Connections to Subsequent Blocks", "Appendix A. EB-vs-FB Notation", "Appendix B. EB-vs-FB Hypothesis Table", "References Cited in This Block" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "vop08_geometric_robustness.Rmd", "filename": "vop08_geometric_robustness.html", "title": "Geometric Robustness of Sampling (Block RG)", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "Why this block exists", "The conceptual bridge (geometry of the user's document ↔ sampling)", "An organic critical reading of the source documents", "The taxonomy: pathologies as a hierarchy of geometries", "The diagnostic (RG.1): size-invariant signals", "The engine and the ladder (RG.2--RG.4)", "Level 0 -- Euclidean diagonal (the default)", "Level 1 -- Euclidean dense", "Level 3 -- Riemannian (Fisher / SoftAbs, and the learned GP-Fisher)", "Level 4 -- Finsler / relativistic (heavy tails)", "Level 5 -- sub-Riemannian (the count; \"the glory\")", "The orchestrator (RG.5): diagnose → select → sample → re-diagnose → escalate or certify", "Option A: the flat direction (RG.6 part i, D96)", "Integration: the bridge and the one-call fit (RG.6 part ii)", "RG.7: applying the capability to the real count, and the certified limit", "The automated fallback (gdpar_geom_laplace(), laplace_fallback)", "Honesty: demonstrated vs. conjectured, and no overreach", "Reproducing the heavy runs" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "v02_gnoseological_validity.Rmd", "filename": "v02_gnoseological_validity.html", "title": "Theoretical Addendum -- Block 2:", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. Purpose", "2. Gnoseological vs. Ontological Status of the Reference", "3. Three Structural Conditions", "3.1. (HOM) No Latent Stratification at the Parameter Level", "3.2. (REG) Regime Stability", "3.3. (CLOS) Operational Closure of the Regime", "4. Three Layers of Gnoseological Validity", "5. Proposition 2A: Algebraic-Functional Status of the Reference", "6. Lemma 2B: Statistical Consistency of the Empirical Reference Estimator", "7. Proposition 2C: Diagnostic Battery for the Three Conditions", "7.1. Test for (HOM): Latent Mixture in the Posterior of $\\theta_i$", "7.2. Test for (REG): Change-Point Detection on Fitted References", "7.3. Test for (CLOS): Extreme-Deviation Profile Inspection", "7.4. Joint Verdict", "8. When Conditions Fail: Operational Consequences", "9. Implementation Implications", "9.1. Path 1 (Hierarchical Bayesian via Stan)", "9.2. Path 2 (Varying-Coefficient via mgcv)", "9.3. Path 3 (Hypernetwork via torch)", "10. Summary", "11. Connections to Subsequent Blocks", "Appendix A. Notation", "Appendix B. Acronyms and Hypotheses", "References Cited in This Block" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "vop04_amm_intermediate.Rmd", "filename": "vop04_amm_intermediate.html", "title": "Intermediate AMM Specifications: B-spline W Bases and Heterogeneous Families per Slot", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. What this vignette covers", "2. B-spline W bases", "2.1. When to switch from polynomial to B-spline", "2.2. API", "2.3. End-to-end example", "2.4. Diagnostics and known limitations", "3. Heterogeneous families per slot (K > 1)", "3.1. The default homogeneous case", "3.2. When to declare heterogeneous families", "3.3. API", "3.4. End-to-end example", "3.5. Identifiability and information diagnostics", "4. Combining B-spline W with heterogeneous families", "5. Known limitations and future work", "References" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "vop01_parametrization_toggle.Rmd", "filename": "vop01_parametrization_toggle.html", "title": "Parametrization Toggle: Operational Guide", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. What this vignette covers", "2. API", "2.1. Global flag", "2.2. Per-component overrides", "2.3. When to use which mode", "2.4. Anchor configuration", "3. The \"auto\" decision: three filters", "3.1. Filter 4 — Divergence attribution", "3.2. Filter 5 — E-BFMI", "3.3. Filter 6 — Info ratio (Path B')", "3.3.1. Effective coefficients and reference scale", "3.3.2. Log info ratio and block bootstrap", "3.3.3. Z-tests and thresholds", "4. Introspecting the decision", "5. Worked example", "6. Known limitations", "6.1. Confounding between a and W when covariates overlap", "6.2. Identifiability of individual basis components of W", "6.3. Block bootstrap versus naive bootstrap", "6.4. Pre-flight wall-time cost", "7. Summary recommendations", "References" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "vop03_grouped_anchors.Rmd", "filename": "vop03_grouped_anchors.html", "title": "Per-group hierarchical anchors: Operational Guide", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. What this vignette covers", "2. API", "2.1. The group argument", "2.2. What changes inside the model", "3. Pre-flight condition (C7)", "4. Coefficient summary under grouping", "5. Cross-group prediction", "6. Interaction with the parametrization toggle", "7. Reproducibility checklist", "8. See also" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "v08_cate_ite_positioning.Rmd", "filename": "v08_cate_ite_positioning.html", "title": "Theoretical Addendum -- Block 8:", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. Purpose", "2. Setting and Notation", "2.1. AMM and CATE/ITE: Formal Specifications", "2.2. The Object-Level Distinction", "2.3. Notation Summary", "3. Three Layers of Positioning", "4. Proposition 8A: The Conceptual Map", "5. Theorem 8B: The AMM-to-CATE Bridge as an Explicit Reparametrization", "6. Proposition 8C: Conceptual Placement of CATE Meta-Learners within AMM", "7. Proposition 8D: Decision-Theoretic Position and the \"Fundamental Problem\"", "8. Recommendation by Scenario", "9. Open Questions", "10. Implementation Implications", "10.1. Bridge invocation", "10.2. Reporting", "10.3. Comparison with meta-learner libraries", "11. Summary", "12. Connections to Subsequent Blocks", "Appendix A. CATE/ITE Notation", "Appendix B. CATE/ITE Hypothesis Table", "References Cited in This Block" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "v00_framework_overview.Rmd", "filename": "v00_framework_overview.html", "title": "Predictive Models with Dynamic Individual Parameters:", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. Motivation: From Cognition to Statistics", "1.1. An Empirical Observation", "1.2. The Cognitive Structure of the Process", "1.3. What Distinguishes This Process from a Standard Model", "2. General Mathematical Formulation", "2.1. Fundamental Equation", "2.2. Individual Prediction", "2.3. Components of the Framework", "3. Path 1: Hierarchical Bayesian Model with Covariate-Dependent Random Effects", "3.1. General Description", "3.2. Hierarchical Specification", "3.2.1. Population Level", "3.2.2. Individual Level", "3.2.3. Observation Level", "3.3. Extension for Zero Inflation", "3.3.1. Structural vs. Sampling Zeros", "3.3.2. Mixture Model with Individual Deviation", "3.4. Extension for Dependence Among Observations", "3.5. Multivariate Extension", "3.6. Recommended Implementation", "4. Path 2: Varying-Coefficient Models", "4.1. General Description", "4.2. Formulation", "4.3. Relation to the Population Reference", "4.4. Estimation of $\\beta(\\cdot)$", "4.5. Strengths", "4.6. Limitations", "4.7. Extension for Zero Inflation", "4.8. Extension for Dependence", "5. Path 3: Conditional Parameter Networks (Hypernetworks / Amortized Inference)", "5.1. General Description", "5.2. Formulation", "5.3. Mechanism of Anchoring to the Population Reference", "5.4. Learning Process", "5.5. Extension for Zero Inflation", "5.6. Extension for Dependence", "5.7. Multivariate Extension", "5.8. Strengths", "5.9. Limitations", "5.10. Recommended Implementation", "6. Comparative Table of the Three Paths", "7. Selection Guide by Scenario", "8. Formal Summary", "8.1. Central Principle", "8.2. Canonical Equation", "8.3. Desirable Properties", "8.4. Universality across Data Conditions", "Appendix: Notation" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "vop00_quickstart.Rmd", "filename": "vop00_quickstart.html", "title": "Quickstart: A First Fit in Five Minutes", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. Who this vignette is for", "2. Installation", "3. A synthetic dataset", "4. A first fit", "5. Reading the print", "6. Coefficients", "7. Predictions", "8. Diagnostics", "9. What to read next", "10. Summary" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "vop03_regression_testing.Rmd", "filename": "vop03_regression_testing.html", "title": "Regression Testing of MCMC Outputs (Experimental)", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. What this vignette covers", "2. The four-layer snapshot schema", "2.1. Layer A — Structural (class signatures, shapes)", "2.2. Layer B — Discrete (bit-exact integer diagnostics)", "2.3. Layer C — Continuous (Monte Carlo standard error tolerance)", "2.4. Layer D — Sanity (absolute floors)", "2.5. The parametrization_resolved audit field", "3. End-to-end workflow", "3.1. Step 1 — Snapshot a reference fit", "3.2. Step 2 — Persist the snapshot", "3.3. Step 3 — Compare a fresh fit against the golden", "4. Tuning k_sigma and sanity_floor", "4.1. k_sigma: sensitivity of layer C", "4.2. sanity_floor: domain-specific absolute thresholds", "4.3. Combining k_sigma and sanity_floor", "5. Integration with testthat", "6. Workflow recommendations", "7. Known limitations", "7.1. Experimental status", "7.2. Stan implementation drift", "7.3. RNG seed semantics across hardware", "7.4. Layer C operates on posterior means only", "7.5. The structural layer is not exhaustive", "8. References and cross-references" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "v03_special_cases.Rmd", "filename": "v03_special_cases.html", "title": "Theoretical Addendum -- Block 3:", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. Purpose", "2. Setting and Notation", "2.1. Reminder of (D-ID) and its concrete verification", "3. Theorem 3.1: Standard Linear Regression", "4. Theorem 3.2: Hierarchical Linear Model with Covariates in Random Effects", "4.1. Per-group anchor as random intercept (Block 6.5)", "5. Theorem 3.3: Random-Coefficient Model", "6. Theorem 3.4: Hastie-Tibshirani Varying-Coefficient Model", "7. Theorem 3.5: Reference-Modulated Varying-Coefficient Model", "8. Theorem 3.6: Hierarchical Bayesian with Multiplicative Interaction", "9. Proposition 3.7: Hypernetwork Models", "10. Summary of Subsumption Map", "11. Connections to Subsequent Blocks", "Appendix A. Verification Checklist Template", "References Cited in This Block" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "v01_amm_identifiability.Rmd", "filename": "v01_amm_identifiability.html", "title": "Theoretical Addendum -- Block 1:", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. Purpose", "2. Setting and Notation", "3. The AMM Hierarchy: Stratifying the Space of Deviation Forms", "3.1. Level 0 -- Degenerate", "3.2. Level 1 -- Linear Additive", "3.3. Level 2 -- Canonical AMM", "3.4. Level 2.5 -- Full-Matrix Multiplicative", "3.5. Level 3 -- Quadratic Extensions", "3.6. Level $K$ -- Polynomial Closure", "3.7. Level $\\infty$ -- Universal AMM", "3.8. Approximation Scheme: From Polynomial AMM to Continuous Functions", "4. Why Level 2 Is the Canonical Default", "5. Standing Assumptions", "6. Identifiability of Canonical AMM", "6.1. Three Layers of Identifiability", "6.2. The Functional Independence Condition", "6.3. Theorem 1A: Algebraic-Functional Identifiability", "6.3.1. Proof of Theorem 1A", "Step 1: Marginal expectation identifies $\\theta_*$.", "Step 2: Subtract the common reference and form differences.", "Step 3: Abstract FIC at $\\theta_*$ forces each difference to zero.", "6.3.2. Necessity of FIC", "6.4. Lemma 1B: Statistical Bridge", "6.5. Corollary 1: Centering of the Framework", "6.6. Proposition 1C: Numerical Verifiability via the Gram Matrix", "6.6.1. Condition C4-bis: Cross-Coordinate Identifiability for p > 1", "6.6.1.1. The failure mode", "6.6.1.2. The condition", "6.6.1.3. Why the extended Gram matrix cannot detect C4-bis", "6.6.1.4. The implementation: rigor = \"full\" vs rigor = \"fast\"", "6.6.1.5. Pedagogical example: overlap that fails (rigor = \"full\")", "6.6.1.6. Pedagogical example: coord-wise disjoint that passes", "6.6.1.7. Worked design pattern: when overlap is unavoidable", "6.6.1.8. Summary", "6.6.2. Condition C7: Anti-aliasing under per-group anchors (Block 6.5)", "6.6.2.1. The failure mode", "6.6.2.2. The condition", "6.6.2.3. Implementation", "6.6.2.4. Pedagogical example: factor(group) in a fails (C7)", "6.6.2.5. Worked design pattern: when a column is constant per group", "6.6.2.6. Summary", "6.7. Theorem 1E: Identifiability of $W$ on the Prior Support", "6.8. Proposition 1F: Scope and Limits of Identifiability for the Hypernetwork (Path 3)", "6.8.1. Empirical Protocol for Discriminating between Richer Structure and Non-Identifiability", "6.8.2. Decision Rule", "7. Identifiability of Higher Levels of the Hierarchy", "8. Standard Models as Special Cases", "8.1. Standard Regression", "8.2. Random-Coefficient Model", "8.3. Varying-Coefficient Model (Hastie and Tibshirani 1993)", "8.4. Hierarchical Bayesian with Multiplicative Interaction", "8.5. Full Canonical AMM", "8.6. Hypernetwork", "9. Test for Component Selection", "9.1. Bayesian Selection via Stratified PSIS-LOO", "9.2. Frequentist Selection via Generalized LRT", "9.3. Hypernetwork Component Norms", "9.4. Reported Selection Output", "10. Implementation Implications", "10.1. Path 1: Hierarchical Bayesian via Stan", "10.2. Path 2: Varying-Coefficient via mgcv", "10.3. Path 3: Amortized Inference via torch", "11. Summary of the Block", "12. Connections to Subsequent Blocks", "Appendix A. AMM Notation", "Appendix B. Acronyms and Hypotheses", "References Cited in This Block" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "vop07_eb_workflow.Rmd", "filename": "vop07_eb_workflow.html", "title": "The Empirical-Bayes Workflow in gdpar", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. What this vignette covers", "2. Setup", "3. Minimal gdpar_eb() call (K = 1 + p = 1)", "4. Path A (K = 1, p > 1): multivariate outcome", "5. Path B (K > 1, p = 1): multi-parametric family", "6. Path C (K > 1, p > 1): full K x p extension", "7. Numerical diagnostics: how to read them", "8. EB vs FB comparison via gdpar_compare_eb_fb()", "9. Troubleshooting", "9.1. gdpar_eb_numerical_error: kappa = ...", "9.2. gdpar_unsupported_feature_error on Path C", "9.3. Multi-start dispersion warning", "9.4. Path B conditional HMC instability under logit-strict links", "9.5. Path C smoke validation", "10. Where to go next", "References" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "v08b_cate_ite_bridge_implementation.Rmd", "filename": "v08b_cate_ite_bridge_implementation.html", "title": "Theoretical Addendum -- Block 8.5.A:", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. Purpose and position in the package", "2. Notation inherited from v08 (rapid reference)", "3. Definition: the T-learner AMM-side", "4. Identification assumptions", "4.1. Inherited from v08", "4.2. Residual no-confounding (T-learner-specific)", "4.3. What the bridge does not assume", "5. Estimation", "6. Inference: per-observation credible bounds and the ATE", "7. Identifiability per arm", "8. Minimum reproducible example", "9. Limitations of the T-learner", "10. Open questions (O-CATE)*", "Appendix A. Notational correspondence with Kuenzel et al. (2019)", "Appendix B. Implementation notes for future external comparators (8.5.B preview)", "References cited in this addendum" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 }, { "source": "v08c_meta_learner_comparison.Rmd", "filename": "v08c_meta_learner_comparison.html", "title": "Theoretical Addendum -- Block 8.5.B:", "author": "José Mauricio Gómez Julián", "engine": "knitr::rmarkdown", "headings": [ "1. Purpose and position in the package", "2. Notation inherited from v08 and v08b (rapid reference)", "3. The pluggable adapter contract", "4. Identification under cross-method comparison", "5. Concordance criterion", "6. Identifiability per arm under the bridge", "7. Limits of the comparison", "8. Minimum reproducible example (CRAN-valid, R-only)", "9. Open questions (O*-CMP)", "Appendix A. Notational correspondence with the meta-learner literature", "Appendix B. Implementation notes for additional adapters", "References cited in this addendum" ], "created": "2026-07-15 18:10:02", "modified": "2026-07-15 18:10:02", "commits": 1 } ], "_score": 4.3979400086720375, "_indexed": true, "_nocasepkg": "gdpar", "_universes": [ "cran", "isadorenabi" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.1.0", "date": "2026-07-15T21:47:45.000Z", "distro": "resolute", "commit": "2be5392367992d694e7afaac59d51024b9ab59bd", "fileid": "https://r2.ropensci.org/1d89867e71d4faa4e78afec0fcde0776abe80a5995dbb7c7c53bcc5434e1fcd6", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/cran/actions/runs/29452652921" }, { "r": "4.6.1", "os": "linux", "version": "0.1.0", "date": "2026-07-15T21:48:05.000Z", "distro": "resolute", "commit": "2be5392367992d694e7afaac59d51024b9ab59bd", "fileid": "https://r2.ropensci.org/e309cc9e4880fab00df284f40fab1dde065e0892535394298cfaaac08080fff7", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/cran/actions/runs/29452652921" } ] }