metaFIML to handle missing covariates

Dear Dr. Cheung and forum pannels,

This is Jihyun, a doctoral student.

I have a question about (currently) two approaches to use FIML to handle missing covariates in meta-regression.

At first, I had adopted the code in Dr. Cheung's book (2015) using RAM formulation and recently also tried to use metaFIML after it was introduced. However, I noticed that the two approaches showed pretty big differences in the results (parameter estimates and standard error estimates).

Could you see my code if I did something wrong? Or, since you noted that metaFIML maybe not stable, should we not use the function yet?
In the dataset, smd is the effect size estimate, v is variance, V1 is the covariate.

I enclosed a data misdat_example.rds and code (FIML_example.R) with FIML approach 1 and approach 2.

I appreciate any of your comments!
Thank you so much.
And again, thank you for distributing metaSEM package!