Links[{"label":"gdpar.html","section":"","type":"","url":"https://cran.r-project.org/web/packages/gdpar/refman/gdpar.html"},{"label":"gdpar.pdf","section":"","type":"","url":"https://cran.r-project.org/web/packages/gdpar/gdpar.pdf"},{"label":"Predictive Models with Dynamic Individual Parameters: A Unifying Conceptual Framework","section":"","type":"","url":"https://cran.r-project.org/web/packages/gdpar/vignettes/v00_framework_overview.html"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/gdpar/vignettes/v00_framework_overview.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/gdpar/vignettes/v00_framework_overview.R"},{"label":"The AMM Canonical Form and Identifiability Conditions","section":"","type":"","url":"https://cran.r-project.org/web/packages/gdpar/vignettes/v01_amm_identifiability.html"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/gdpar/vignettes/v01_amm_identifiability.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/gdpar/vignettes/v01_amm_identifiability.R"},{"label":"Gnoseological Validity Conditions for the Population Reference","section":"","type":"","url":"https://cran.r-project.org/web/packages/gdpar/vignettes/v02_gnoseological_validity.html"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/gdpar/vignettes/v02_gnoseological_validity.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/gdpar/vignettes/v02_gnoseological_validity.R"},{"label":"Standard Predictive Models as Formal Special Cases of the AMM","section":"","type":"","url":"https://cran.r-project.org/web/packages/gdpar/vignettes/v03_special_cases.html"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/gdpar/vignettes/v03_special_cases.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/gdpar/vignettes/v03_special_cases.R"},{"label":"Asymptotic Theory for Path 1 (Hierarchical Bayesian)","section":"","type":"","url":"https://cran.r-project.org/web/packages/gdpar/vignettes/v04_asymptotics_path1_bayesian.html"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/gdpar/vignettes/v04_asymptotics_path1_bayesian.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/gdpar/vignettes/v04_asymptotics_path1_bayesian.R"},{"label":"Asymptotic Theory for Path 2 (Varying-Coefficient via Penalized Splines)","section":"","type":"","url":"https://cran.r-project.org/web/packages/gdpar/vignettes/v05_asymptotics_path2_vcm.html"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/gdpar/vignettes/v05_asymptotics_path2_vcm.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/gdpar/vignettes/v05_asymptotics_path2_vcm.R"}]
TextReference 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