[R-meta] clarification on "V" in "rma.mv()" from the "metafor" package

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Dear All,

I had some clarification questions regarding the "rma.mv()" from the
"metafor" package.

In regular (i.e., non-meta-regression) multivariate multilevel models, we
naturally get an estimated variance-covariance matrix for the DV values
across different levels of the grouping variable (ID) by specifying the
random effects:

nlme::lme(DV_values ~ DV_indx-1, random = ~ DV_indx -1 | ID,
          data = data, correlation = NULL, weights = NULL)     ## DON'T RUN

But in "rma.mv()", there is an additional "V" argument to provide a list of
known/guesstimated variance-covariance matrices between the DV values
[i.e., individual effect sizes] in each study (i.e., grouping variable) as
well.

The R documentation on the "V" argument in "rma.mv()" is very terse. But,

(1) Does the use of "V" arise whenever each study generally produces
multiple dependent effect sizes OR it is reserved for when we have a pool
of e.g., multi-outcome and/or longitudinal studies?


(2) Given the lack of an extended documentation, is/are there any general
equivalent(s) for the "V" argument in the context of regular (i.e.,
non-meta-regression) multilevel modeling packages (e.g., combination of
"correlation" and "weights" arguments from the "nlme::lme()")?


(3) Why the multi-level structure alone can't account for the correlations
among effect sizes within each study needing us to specify an additional
"V" list of  variance-covariance matrices?

Thank you very much for your knowledge and expertise,
Tim

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