Variational Inference for Hierarchical Generalized Linear Models
Get samples from GLMER
Interpret a vglmer formula for splines
To be used if subbars fails, usually when there is an argument to v_s ...
Code from Wand and Ormerod (2008) Found here here: 10.1111/j.1467-842X...
Linear Regression by Cholesky
Perform MAVB after fitting vglmer
Draw samples from the variational distribution
Predict Methods for random walk smooth
Objects exported from other packages
Simple EM algorithm for starting values.
SuperLearner with (Variational) Hierarchical Models
Constructor for random walk smooth
Create splines for use in vglmer
Variance of Rows or Columns of Matrices
Control for vglmer estimation
Predict after vglmer
Generic Functions after Running vglmer
Variational Inference for Hierarchical Generalized Linear Models
Estimates hierarchical models using mean-field variational Bayes. 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.