bbolker
commented
10 months ago Thanks! This is way more detail/support than you needed to provide, but the effort is appreciated.
Any chance you can provide a pull request? (If not, that's OK, but a PR will definitely be the fastest way to get the updates included ...) All the advantages you listed are relevant, but I would suggest that you try to make the description of the package fairly brief (one or two sentences/a few lines)
I would like to propose a few items to be added to the View in the Generalized estimating equations section.
The glmtoolbox that I found recently is awesome. 1) unlike geepack (no new features, no bug fixes, frozen/zombie state), glmtoolbox it's actively developed. Only it's a pity it lacks a GitHub repo, which would facilitate reporting issues a lot. Maybe you could try to convince prof. Vanegas to share it on GitHub well? 2) unlike geepack, it doesn't crash (prints informative warning) when the unstructured cov. is combined with waves (useful when analysing longitudinal data with missing visits) 3) supports more covariance structures than any other package 4) provides leverage, local.influence, DFBETA and Cook's distance out of the box 5) offers model-comparison based anova through Wald's and generalized score tests. 6) unlike geepack, it well supports IPW (inverse-probability) weighting for dropouts out of the box (one has to specify the model for MAR dropouts), no need to play separately with the ipw package. Works on both observation and cluster level. 7) offers a module for non-linear GEE 8) predict() is available 9) QIC and RJC (Rotnitzky-Jewell's) criterion out of the box 10) Mahalanobis', Pearson's and deviance residuals 11) forward/backward stepwise variable selection 12) various var.cov estimators are available: robust (Sandwich), df-adjusted, model (naive), bias-corrected (small-sample Mancl-DeRouen) and jackknife. 13) gives results consistent with other packages (only minor discrepancies) 14) It well integrates with emmeans via qdrg(), so advanced contrasts over LS-means can be tested, also with MVT adjustment.
Personally speaking, glmtoolbox seems the new king and a standard for GEE in R, outperforming geepack in most aspects.
The other proposed packages, MIIPW, CRTgeeDR, drgee, geeCRT, are important from the clinical trials perspective, offering ways to deal with data missing at random (MAR).