check by shard | Profile derivative checks | Joint density derivative checks
Package: adlaplace 0.5.2
adlaplace: Laplace Approximations Using Automatic Differentiation
Computes Laplace approximations for hierarchical models using automatic differentiation (CppAD) and trust-region optimization.
Authors:
adlaplace_0.5.2.tar.gz
adlaplace_0.5.2.zip(r-4.7)adlaplace_0.5.2.zip(r-4.6)adlaplace_0.5.2.zip(r-4.5)
adlaplace_0.5.2.tgz(r-4.6-x86_64)adlaplace_0.5.2.tgz(r-4.6-arm64)adlaplace_0.5.2.tgz(r-4.5-x86_64)adlaplace_0.5.2.tgz(r-4.5-arm64)
adlaplace_0.5.2.tar.gz(r-4.7-arm64)adlaplace_0.5.2.tar.gz(r-4.7-x86_64)adlaplace_0.5.2.tar.gz(r-4.6-arm64)adlaplace_0.5.2.tar.gz(r-4.6-x86_64)
adlaplace_0.5.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
adlaplace/json (API)
| # Install 'adlaplace' in R: |
| install.packages('adlaplace', repos = c('https://eborgnine.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/eborgnine/adlaplace/issues
Last updated from:abf0625826 (on main). Checks:13 OK. Indexed: yes.
A new build is currently in progress.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 214 | ||
| linux-devel-x86_64 | OK | 214 | ||
| source / vignettes | OK | 401 | ||
| linux-release-arm64 | OK | 252 | ||
| linux-release-x86_64 | OK | 236 | ||
| macos-release-arm64 | OK | 122 | ||
| macos-release-x86_64 | OK | 327 | ||
| macos-oldrel-arm64 | OK | 134 | ||
| macos-oldrel-x86_64 | OK | 339 | ||
| windows-devel | OK | 276 | ||
| windows-release | OK | 248 | ||
| windows-oldrel | OK | 244 | ||
| wasm-release | OK | 166 |
Exports:.type_factor_levelsad_dataad_funad_fun_ptrad_shardsadlaplaceapply_theta_logbeta_infobinomialby_groupclone_ad_fun_ptrcollect_termsdata_setupdesignelgm_matrixextra_ad_funformat_parametersfpolyfun_obj_fdfhgaussiangradhessianiidinner_optinterceptiwpjoint_log_denslaplace_half_H_invlinearlog_lik_laplacemean_mvquadmodel_datanbinomouter_fnouter_grprecisionrandom_inforef_alignrmvnldlrpolysim_fitsizestheta_infotrace_hinv_t
Dependencies:data.tablelatticeMatrixRcppRCppADRcppEigentrustOptim
GAMM Examples
Last update: 2026-07-17
Started: 2026-07-17
BYM disease mapping: Germany oral cavity cancer
Introduction | Data | Building AD shards | Combining shards | Joint log density derivatives | Inner optimization | Joint density derivative checks | Profile likelihood | Outer optimization | Spatial effects
Last update: 2026-07-17
Started: 2026-07-17
Laplace Approximations with adlaplace
Model | Simulated data | Building AD shards | Observations and hyperparameters | Random effects | Observation likelihood check | Combining shards | Joint log density derivatives | Inner optimization | likelihood | Outer optimization | Derivative checks | Joint density derivative checks | nicer interface | mean estimation
Last update: 2026-07-17
Started: 2025-11-26
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Valid model type factor levels | .type_factor_levels |
| Build design matrices and parameter metadata from a formula | ad_data data_setup |
| Per-shard layout for AD density evaluation | ad_data-class |
| Build AD function with Hessian templates | ad_fun ad_fun,ad_data-method ad_fun,ad_fun_ptr-method ad_fun,list-method |
| Build raw AD handle for one density shard | ad_fun_ptr |
| Raw AD handle (external pointer) | ad_fun_ptr-class |
| AD function with Hessian templates attached | ad_fun-class |
| Partition observations into AD shards by sparsity pattern | ad_shards |
| Fit a hierarchical model by Laplace-approximate maximum likelihood | adlaplace |
| C++ backend entry points | adlaplace_cpp fun_obj_fdfh grad hessian joint_log_dens trace_hinv_t |
| Methods for fitted adlaplace models | adlaplace_fit-methods coef.adlaplace_fit confint.adlaplace_fit fitted.adlaplace_fit logLik.adlaplace_fit nobs.adlaplace_fit plot.adlaplace_fit predict.adlaplace_fit print.adlaplace_fit print.summary.adlaplace_fit summary.adlaplace_fit vcov.adlaplace_fit |
| Apply log transform to selected theta columns | apply_theta_log |
| Binomial observation term | beta_info,binomial-method binomial binomial-class design,binomial-method precision,binomial-method random_info,binomial-method theta_info,binomial-method |
| By-Group Classes and Functions | by_group |
| By-Group Class | by_group-class |
| Combine 'ad_fun_ptr' handles | c.ad_fun_ptr |
| Deep copy of an 'ad_fun_ptr' handle | clone_ad_fun_ptr |
| Parse Model Terms from Formula | collect_terms |
| Format Laplace estimates using parameter metadata | format_parameters |
| Fixed Polynomial Model Term | beta_info,fpoly-method design,fpoly-method fpoly fpoly-class precision,fpoly-method random_info,fpoly-method theta_info,fpoly-method |
| Example GAMM simulation data | dat gamm |
| Gaussian observation term | beta_info,gaussian-method design,gaussian-method gaussian gaussian-class precision,gaussian-method random_info,gaussian-method theta_info,gaussian-method |
| Germany oral cavity cancer (Besag-York-Mollie example) | germany |
| IID Random Effects Term | beta_info,iid-method design,iid-method iid iid-class precision,iid-method random_info,iid-method theta_info,iid-method |
| Inner optimization over gamma using trust-region CG (sparse) | inner_opt |
| intercept Model Term | beta_info,intercept-method design,intercept-method intercept intercept-class precision,intercept-method random_info,intercept-method theta_info,intercept-method |
| Integrated Wiener Process Term Constructor | iwp |
| Integrated Wiener Process Term | beta_info,iwp-method design,iwp-method iwp-class precision,iwp-method random_info,iwp-method theta_info,iwp-method |
| Extract inner inverse Cholesky factor from Laplace output | laplace_half_H_inv |
| Linear Model Term | beta_info,linear-method design,linear-method linear linear-class precision,linear-method random_info,linear-method theta_info,linear-method |
| Log-likelihood with inner Laplace optimization | log_lik_laplace |
| Posterior mean of random effects via multivariate quadrature | mean_mvquad |
| Assemble model terms and per-shard 'ad_data' objects | model_data |
| Base Model Class | model-class |
| Model Term Generics | beta_info beta_info,model-method design design,model-method elgm_matrix extra_ad_fun extra_ad_fun,model-method model-generics precision precision,model-method random_info random_info,model-method theta_info theta_info,model-method |
| Negative binomial observation term | beta_info,nbinom-method design,nbinom-method nbinom nbinom-class precision,nbinom-method random_info,nbinom-method theta_info,nbinom-method |
| Outer objective and gradient wrappers | outer_fn outer_gr outer_optim_wrappers |
| Simulate from a multivariate normal using LDL of the precision matrix | rmvnldl |
| Random Polynomial Model Term | beta_info,rpoly-method design,rpoly-method precision,rpoly-method random_info,rpoly-method rpoly rpoly-class theta_info,rpoly-method |
| Simulate linear predictors on a new covariate grid | sim_fit |
| Parameter-block row counts for an 'ad_data' | sizes sizes,ad_data-method |
