*** IMPORTANT: summary(object) reported the incorrect
variational mean of q(Sigma_j) [variance components]. This has been
fixed; nothing changes in estimation and object$cov has
always extracted the correct variational distribution of q(Sigma_j).
Thank you to Ruggero Bellio for bringing this to my attention.
** Added convergence criterion using “relative” ELBO, i.e. stopping when ELBO/N increases by a small amount where N is the number of observations. Default option is 0 but one can enable it on large datasets.
** Adjustments to “print”, e.g. only showing change in q(alpha,beta) at convergence
Small adjustments to sparse matrix algebra to improve speed in problems with high-dimensional REs. Thank you to Ruggero Bellio and Hanna Niwinska for finding datasets that illustrate this problem.
Updates to README to reference new papers and experimental package branches.
Address certain warnings raised by Matrix-package
updates.
Removes unnecessary model preparation steps for
parameter_expansion="translation" and
factorization_method="strong". Improves speed on default
settings for models with many random effects.
Updated references in documentation.
Small adjustment to tests to prevent failure from update to
waldo package
** Add gKRLS as an option for smoothing multiple (continuous) covariates. Chang and Goplerud (2024; https://doi.org/10.1017/pan.2023.27) provides more details.
Adjust predict.vglmer to allow for faster
predictions on large datasets by not copying and filling in a large
sparse matrix. Thank you to Michael Auslen for pointing out this
issue.
Add the option for terms to predict to
allow for predictions for each random effect separately
Address a bug where predictions with NA in new
data.frame would fail for certain splines or for cases where
newdata had a single row.
vglmer to not throw deprecation messages with
Matrix 1.5. Thank you to Mikael Jagan for suggestions on how to adapt
the code.