gdpar: General Dynamic Parameter Models via Reference Anchoring

Implements a unified predictive framework in which individual parameters are decomposed as theta_i equal to theta_ref plus Delta(x_i, theta_ref), with theta_ref a population reference and Delta an explicit deviation function. The decomposition follows the Additive-Multiplicative-Modulated canonical form and is estimated through three complementary paths: hierarchical Bayesian inference via 'Stan', varying-coefficient models via penalized splines, and amortized inference via hypernetworks in 'torch'. The package provides identifiability diagnostics, validity tests for the population reference, and benchmarks against canonical zero-inflated count datasets and avian abundance data from the eBird Status and Trends project. The framework and its estimation paths are described in Gomez Julian (2026) <doi:10.5281/zenodo.21046269>.

Version: 0.1.0
Depends: R (≥ 4.2.0)
Imports: posterior, stats, methods, withr
Suggests: cmdstanr, loo, bayesplot, DHARMa, digest, mgcv, Matrix, pscl, AER, ebirdst, knitr, rmarkdown, testthat (≥ 3.0.0), kableExtra, brms, INLA, rstanarm, scoringRules, gratia, grf, reticulate, mvnfast
Published: 2026-07-15
DOI: 10.32614/CRAN.package.gdpar (may not be active yet)
Author: José Mauricio Gómez Julián ORCID iD [aut, cre]
Maintainer: José Mauricio Gómez Julián <isadore.nabi at pm.me>
BugReports: https://github.com/IsadoreNabi/gdpar/issues
License: GPL (≥ 3)
URL: https://github.com/IsadoreNabi/gdpar
NeedsCompilation: no
Additional_repositories: https://stan-dev.r-universe.dev, https://inla.r-inla-download.org/R/stable
Materials: README, NEWS
CRAN checks: gdpar results

Documentation:

Reference manual: gdpar.html , gdpar.pdf
Vignettes: Predictive Models with Dynamic Individual Parameters: A Unifying Conceptual Framework (source, R code)
The AMM Canonical Form and Identifiability Conditions (source, R code)
Gnoseological Validity Conditions for the Population Reference (source, R code)
Standard Predictive Models as Formal Special Cases of the AMM (source, R code)
Asymptotic Theory for Path 1 (Hierarchical Bayesian) (source, R code)
Asymptotic Theory for Path 2 (Varying-Coefficient via Penalized Splines) (source, R code)
Asymptotic Theory for Path 3 (Hypernetwork) (source, R code)
Empirical Bayes vs. Fully Bayes Treatment of the Population Reference (source, R code)
Empirical Bayes vs. Fully Bayes – Multivariate Extension (source, R code)
Positioning AMM relative to the CATE / ITE Literature (source, R code)
The T-learner AMM-side Causal Bridge (source, R code)
Theoretical Addendum 8.5.B: Comparison against External Meta-learners (source, R code)
AMM Sub-phases 8.3.1 to 8.3.10 – Theoretical Canonization (source, R code)
Cognitive Motivation: From Driver Prediction to Reference-Anchored Individuation (source, R code)
Quickstart: A First Fit in Five Minutes (source, R code)
Parametrization Toggle: Operational Guide (source, R code)
Arbitrary p: Operational Cookbook for Multivariate Fits (source, R code)
Per-group hierarchical anchors: Operational Guide (source, R code)
Regression Testing of MCMC Outputs (Experimental) (source, R code)
Intermediate AMM Specifications: B-spline W Bases and Heterogeneous Families per Slot (source, R code)
Distributional Regression K > 1 and Residual Diagnostics with DHARMa (source, R code)
Comparing the AMM-side T-learner against External Meta-learners (source, R code)
The Empirical-Bayes Workflow in gdpar (source, R code)
Geometric Robustness of Sampling (Block RG) (source, R code)
Dependence-Robust Inference in gdpar (Axis 2) (source, R code)

Downloads:

Package source: gdpar_0.1.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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