The semTools package has a Missing Data suite, which for years included utilities for fitting structural equation models to multiply imputed data sets with the lavaan package and automatically pooling the results. These multiple-imputation features continue to grow, justifying their own separate package (and current deprecation in semTools).
runMI() function does not exist. The semTools::runMI() function had a fun= argument to specify which lavaan function to call (lavaan() or the cfa() and sem() wrappers). Instead, analogous to lavaan, the basic function will be lavaan.mi(), with cfa.mi() and sem.mi() as wrappers around lavaan.mi().semTools::runMI(), users may not impute data indirectly via miPackage= and miArgs= arguments. This feature was originally included to mimic Mplus, which allows for model-based imputation. Because semTools::runMI() did not use the specified SEM as an imputation model, the mimicry of the Mplus functionality was only cosmetic. Even in semTools::runMI(), it was always better to impute (with full control) using dedicated imputation software first (e.g., the Amelia or mice package, or external software like Blimp), to be analyzed with lavaan.mi().
blavaan package does indirectly allow for model-based imputation, if users explicitly request posterior samples of missing values. It is planned to eventually capitalize on that feature with a dedicated function in the lavaan.mi package.lavaan(test=) argument, there is a new test= argument for lavaan::lavTestLRT(), enabling users to select which specific $\Delta \chi^2$ test should be calculated for a model comparison. Accordingly, the lavTestLRT.mi() function's test= argument has been renamed pool.method=, so that users can still pass a test= argument to lavTestLRT.mi(...).
test= argument to select "D1", "D2", or "D3". The lavaan.mi package has change that argument to pool.method=, as well as implementing the newer "D4" method for pooling likelihood-ratio statistics (see Neww Features section).pool.method = "D4") for lavTestLRT.mi(), replacing the old default (but pool.method = "D3" is still available). The D4 pooling method performs similarly to D3 (see Grund et al.'s simulation study) but is less computationally intensive.lavaan(test=) argument, the user can choose which statistic can be pooled using lavTestLRT.mi(..., pool.method = "D2", pool.robust = TRUE). This is controlled via the new scaled.test= argument. As an alternative to the standard $\chi^2$ statistic, the user can also pool Browne's (1984) residual-based statistic using, e.g., lavTestLRT.mi(..., pool.method = "D2", standard.test = "browne.residual.nt") with the default pool.robust=FALSE.lavaan functionality for pooled results: paramterEstimates.mi(), standardizedSolution.mi(), and lavResiduals.mi().poolSat() function implements Cai and colleagues' (2012, 2019) method of pooling the saturated-model results, then fitting SEM(s) to those pooled summary statistics. This can be much more computationally efficient than fitting each SEM of interest to each imputation and pooling the results of each estimated SEM. Instead, pooling only occurs for the summary statistics, and each SEM is estimated as it would be for complete data.VGAM package was loaded, it created a conflict with finding the anova() method for lavaan.mi objects in semTools. This was resolved in February 2022 when VGAM version 1.1-6 was sent to CRAN.