A B C D E F G H I J L M N O P R S T V misc
| adlaplace | Fit a hierarchical model by Laplace-approximate maximum likelihood |
| adlaplace_cpp | C++ backend entry points |
| adlaplace_fit-methods | Methods for fitted adlaplace models |
| ad_pack | Build AD function with Hessian templates |
| ad_pack-class | AD function with Hessian templates attached |
| ad_pack-method | Build AD function with Hessian templates |
| ad_pack_drop | Retape an AD pack after dropping observation units |
| ad_pack_ptr | Build raw AD handle for one density shard |
| ad_pack_ptr-class | Raw AD handle (external pointer) |
| ad_pack_set_X | Rebuild an 'ad_pack' for a new model-data design |
| aghq_ff | Build 'aghq' objective list from an adlaplace fit |
| amip_set | Approximate most influential removal set (AMIP) |
| apply_theta_log | Apply log transform to selected theta columns |
| beta_info | Model Term Generics |
| beta_info-method | Dirichlet-multinomial response term |
| beta_info-method | Fixed Polynomial Model Term |
| beta_info-method | intercept Model Term |
| beta_info-method | Linear Model Term |
| beta_info-method | Model Term Generics |
| binom | Binomial observation term |
| binomial-class | Binomial observation term |
| by_group | By-Group Classes and Functions |
| by_group-class | By-Group Class |
| c.ad_pack_ptr | Combine 'ad_pack_ptr' handles |
| clone_ad_pack_ptr | Deep copy of an 'ad_pack_ptr' handle |
| coef.adlaplace_fit | Methods for fitted adlaplace models |
| collect_terms | Parse Model Terms from Formula |
| confint.adlaplace_fit | Methods for fitted adlaplace models |
| dat | Example GAMM simulation data |
| density_data | Per-shard layout for AD density evaluation |
| density_data-class | Per-shard layout for AD density evaluation |
| deriv_scan_joint | Scan joint log-density AD derivatives vs finite differences |
| deriv_scan_laplace | Scan Laplace profile AD derivatives vs finite differences |
| design | Model Term Generics |
| design-method | Dirichlet-multinomial response term |
| design-method | Fixed Polynomial Model Term |
| design-method | IID Random Effects Term |
| design-method | intercept Model Term |
| design-method | Integrated Wiener Process Term |
| design-method | Linear Model Term |
| design-method | Model Term Generics |
| design-method | Random Polynomial Model Term |
| dirichlet_multinom | Dirichlet-multinomial case-crossover response term |
| dirichlet_multinom-class | Dirichlet-multinomial response term |
| elgm_matrix | Model Term Generics |
| elgm_matrix-method | Dirichlet-multinomial response term |
| extra_density | Model Term Generics |
| extra_density-method | Model Term Generics |
| fitted.adlaplace_fit | Methods for fitted adlaplace models |
| fit_outer | Outer optimization scaffolding for Laplace profile likelihoods |
| format_parameters | Format Laplace estimates using parameter metadata |
| fpoly | Fixed Polynomial Model Term |
| fpoly-class | Fixed Polynomial Model Term |
| fun_obj_fdfh | C++ backend entry points |
| gamm | Example GAMM simulation data |
| gaussian-class | Gaussian observation term |
| germany | Germany oral cavity cancer (Besag-York-Mollie example) |
| grad | C++ backend entry points |
| grad_obs_units | Per-unit observation gradients (streaming, grad-only tapes) |
| has_openmp | Whether this build was compiled with OpenMP support. |
| hessian | C++ backend entry points |
| hinv_trace_index | Positions of a shard's quadratic form inside the joint inverse Hessian |
| hinv_trace_index-method | Positions of a shard's quadratic form inside the joint inverse Hessian |
| iid | IID Random Effects Term |
| iid-class | IID Random Effects Term |
| influence_scores | Per-unit influence scores for an inner linear functional |
| inner_opt | Inner optimization over gamma using trust-region CG (sparse) |
| intercept | intercept Model Term |
| intercept-class | intercept Model Term |
| iwp | Integrated Wiener Process Term Constructor |
| iwp-class | Integrated Wiener Process Term |
| joint_log_dens | C++ backend entry points |
| laplace_half_H_inv | Extract inner inverse Cholesky factor from Laplace output |
| laplace_loglik | Laplace-approximate log-likelihood (positive convention) |
| linear | Linear Model Term |
| linear-class | Linear Model Term |
| logLik.adlaplace_fit | Methods for fitted adlaplace models |
| log_lik_laplace | Log-likelihood with inner Laplace optimization |
| london | London daily mortality and PM10 (case-crossover example) |
| mean_mvquad | Posterior mean of random effects via multivariate quadrature |
| model-generics | Model Term Generics |
| model_data | Assemble model terms and per-shard 'density_data' objects |
| model_term-class | Base Model Class |
| nbinom | Negative binomial observation term |
| nbinom-class | Negative binomial observation term |
| nobs.adlaplace_fit | Methods for fitted adlaplace models |
| normal | Gaussian observation term |
| n_groups | Number of AD shards in an 'ad_pack_ptr' handle |
| n_obs_units | Number of removable observation units |
| obs_groups | Partition observations into AD shards by sparsity pattern |
| obs_groups_units | Build 'obs_groups' for a subset of observation units |
| outer_fn | Outer objective and gradient wrappers |
| outer_gr | Outer objective and gradient wrappers |
| outer_optim_wrappers | Outer objective and gradient wrappers |
| phi_gamma | Linear functional of the inner field gamma |
| plot.adlaplace_fit | Methods for fitted adlaplace models |
| pois | Poisson observation term |
| poisson-class | Poisson observation term |
| precision | Model Term Generics |
| precision-method | Dirichlet-multinomial response term |
| precision-method | IID Random Effects Term |
| precision-method | intercept Model Term |
| precision-method | Integrated Wiener Process Term |
| precision-method | Linear Model Term |
| precision-method | Model Term Generics |
| precision-method | Random Polynomial Model Term |
| predict.adlaplace_fit | Methods for fitted adlaplace models |
| print.adlaplace_fit | Methods for fitted adlaplace models |
| print.summary.adlaplace_fit | Methods for fitted adlaplace models |
| prior | Hyperparameter prior term |
| prior-class | Hyperparameter prior term |
| random_info | Model Term Generics |
| random_info-method | Dirichlet-multinomial response term |
| random_info-method | IID Random Effects Term |
| random_info-method | intercept Model Term |
| random_info-method | Integrated Wiener Process Term |
| random_info-method | Linear Model Term |
| random_info-method | Model Term Generics |
| random_info-method | Random Polynomial Model Term |
| replace_vars | Replace data-column names in model terms |
| replace_vars-method | Replace data-column names in model terms |
| rmvnldl | Simulate from a multivariate normal using LDL of the precision matrix |
| rpoly | Random Polynomial Model Term |
| rpoly-class | Random Polynomial Model Term |
| sim_fit | Simulate linear predictors on a new covariate grid |
| sim_iid | Simulate IID random effects with pointwise and global intervals |
| sim_random | Simulate a random-effect contribution for one covariate |
| sizes | Parameter-block row counts for an 'density_data' |
| sizes-method | Parameter-block row counts for an 'density_data' |
| summary.adlaplace_fit | Methods for fitted adlaplace models |
| takahashi_davis | Sparse inverse subset of a symmetric positive-definite matrix |
| term_data_setup | Build design matrices and parameter metadata from a formula |
| theta_info | Model Term Generics |
| theta_info-method | Dirichlet-multinomial response term |
| theta_info-method | Gaussian observation term |
| theta_info-method | IID Random Effects Term |
| theta_info-method | Integrated Wiener Process Term |
| theta_info-method | Model Term Generics |
| theta_info-method | Negative binomial observation term |
| trace_hinv_t | C++ backend entry points |
| vcov.adlaplace_fit | Methods for fitted adlaplace models |
| .model_role_levels | Valid model type factor levels |