R: Vignettes and other documentation

# Vignettes and other documentation

## Vignettes from package 'gdpar'

gdpar::v00_framework_overview |  | Predictive Models with Dynamic Individual Parameters: A Unifying Conceptual Framework | HTML | source | R code
gdpar::v01_amm_identifiability |  | The AMM Canonical Form and Identifiability Conditions | HTML | source | R code
gdpar::v02_gnoseological_validity |  | Gnoseological Validity Conditions for the Population Reference | HTML | source | R code
gdpar::v03_special_cases |  | Standard Predictive Models as Formal Special Cases of the AMM | HTML | source | R code
gdpar::v04_asymptotics_path1_bayesian |  | Asymptotic Theory for Path 1 (Hierarchical Bayesian) | HTML | source | R code
gdpar::v05_asymptotics_path2_vcm |  | Asymptotic Theory for Path 2 (Varying-Coefficient via Penalized Splines) | HTML | source | R code
gdpar::v06_asymptotics_path3_hypernetwork |  | Asymptotic Theory for Path 3 (Hypernetwork) | HTML | source | R code
gdpar::v07_eb_vs_fb |  | Empirical Bayes vs. Fully Bayes Treatment of the Population Reference | HTML | source | R code
gdpar::v07b_eb_multivariate |  | Empirical Bayes vs. Fully Bayes -- Multivariate Extension | HTML | source | R code
gdpar::v08_cate_ite_positioning |  | Positioning AMM relative to the CATE / ITE Literature | HTML | source | R code
gdpar::v08b_cate_ite_bridge_implementation |  | The T-learner AMM-side Causal Bridge | HTML | source | R code
gdpar::v08c_meta_learner_comparison |  | Theoretical Addendum 8.5.B: Comparison against External Meta-learners | HTML | source | R code
gdpar::v08d_amm_subphases |  | AMM Sub-phases 8.3.1 to 8.3.10 -- Theoretical Canonization | HTML | source | R code
gdpar::v09_cognitive_motivation |  | Cognitive Motivation: From Driver Prediction to Reference-Anchored Individuation | HTML | source | R code
gdpar::vop00_quickstart |  | Quickstart: A First Fit in Five Minutes | HTML | source | R code
gdpar::vop01_parametrization_toggle |  | Parametrization Toggle: Operational Guide | HTML | source | R code
gdpar::vop02_arbitrary_p |  | Arbitrary p: Operational Cookbook for Multivariate Fits | HTML | source | R code
gdpar::vop03_grouped_anchors |  | Per-group hierarchical anchors: Operational Guide | HTML | source | R code
gdpar::vop03_regression_testing |  | Regression Testing of MCMC Outputs (Experimental) | HTML | source | R code
gdpar::vop04_amm_intermediate |  | Intermediate AMM Specifications: B-spline W Bases and Heterogeneous Families per Slot | HTML | source | R code
gdpar::vop05_distributional_K_dharma |  | Distributional Regression K > 1 and Residual Diagnostics with DHARMa | HTML | source | R code
gdpar::vop06_meta_learner_comparison |  | Comparing the AMM-side T-learner against External Meta-learners | HTML | source | R code
gdpar::vop07_eb_workflow |  | The Empirical-Bayes Workflow in gdpar | HTML | source | R code
gdpar::vop08_geometric_robustness |  | Geometric Robustness of Sampling (Block RG) | HTML | source | R code
gdpar::vop09_dependence_robust |  | Dependence-Robust Inference in gdpar (Axis 2) | HTML | source | R code