Package: vglmer 1.0.6
vglmer: Variational Inference for Hierarchical Generalized Linear Models
Estimates hierarchical models using variational inference. At present, it can estimate logistic, linear, and negative binomial models. It can accommodate models with an arbitrary number of random effects and requires no integration to estimate. It also provides the ability to improve the quality of the approximation using marginal augmentation. Goplerud (2022) <doi:10.1214/21-BA1266> and Goplerud (2024) <doi:10.1017/S0003055423000035> provide details on the variational algorithms.
Authors:
vglmer_1.0.6.tar.gz
vglmer_1.0.6.zip(r-4.5)vglmer_1.0.6.zip(r-4.4)vglmer_1.0.6.zip(r-4.3)
vglmer_1.0.6.tgz(r-4.4-x86_64)vglmer_1.0.6.tgz(r-4.4-arm64)vglmer_1.0.6.tgz(r-4.3-x86_64)vglmer_1.0.6.tgz(r-4.3-arm64)
vglmer_1.0.6.tar.gz(r-4.5-noble)vglmer_1.0.6.tar.gz(r-4.4-noble)
vglmer_1.0.6.tgz(r-4.4-emscripten)vglmer_1.0.6.tgz(r-4.3-emscripten)
vglmer.pdf |vglmer.html✨
vglmer/json (API)
NEWS
| # Install 'vglmer' in R: |
| install.packages('vglmer', repos = c('https://mgoplerud.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mgoplerud/vglmer/issues
Last updated 13 days agofrom:fc71ad6f7b. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 07 2024 |
| R-4.5-win-x86_64 | OK | Nov 07 2024 |
| R-4.5-linux-x86_64 | OK | Nov 07 2024 |
| R-4.4-win-x86_64 | OK | Nov 07 2024 |
| R-4.4-mac-x86_64 | OK | Nov 07 2024 |
| R-4.4-mac-aarch64 | OK | Nov 07 2024 |
| R-4.3-win-x86_64 | OK | Nov 07 2024 |
| R-4.3-mac-x86_64 | OK | Nov 07 2024 |
| R-4.3-mac-aarch64 | OK | Nov 07 2024 |
Exports:add_formula_SLELBOfixefformat_glmerformat_vglmerMAVBposterior_samples.vglmerpredict_MAVBranefSL.glmerSL.vglmerv_svglmervglmer_control
Dependencies:bootCholWishartlatticelme4lmtestMASSMatrixmgcvminqamvtnormnlmenloptrRcppRcppEigenzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Perform MAVB after fitting vglmer | MAVB |
| Draw samples from the variational distribution | posterior_samples.vglmer |
| SuperLearner with (Variational) Hierarchical Models | add_formula_SL predict.SL.glmer predict.SL.vglmer SL.glmer SL.vglmer sl_vglmer |
| Create splines for use in vglmer | v_s |
| Variational Inference for Hierarchical Generalized Linear Models | vglmer |
| Control for vglmer estimation | vglmer_control |
| Predict after vglmer | predict.vglmer predict_MAVB vglmer_predict |
| Generic Functions after Running vglmer | coef.vglmer ELBO fitted.vglmer fixef.vglmer format_glmer format_vglmer formula.vglmer print.vglmer ranef.vglmer sigma.vglmer summary.vglmer vcov.vglmer vglmer-class |
