General Dynamic Parameter Models via Reference Anchoring R package gdpar version 0.1.0 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>. Software 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 José Mauricio Gómez Julián <isadore.nabi@pm.me> Comprehensive R Archive Network (CRAN) José Mauricio Gómez Julián [aut, cre] (ORCID: <https://orcid.org/0009-0000-2412-3150>) GPL (>= 3) 2026-07-15 application/tgz https://CRAN.R-project.org/package=gdpar doi:10.32614/CRAN.package.gdpar