To implement OO or not in R package, and if so, how to structure it?

Hello all,

I am trying to decide how to structure an R package.  Specifically, do I use
OO classes, or just provide functions?  If the former, how should I
structure the objects in relation to the type of data the package is
intended to manage?

I have searched for, but haven't found, resources that guide one in the
*decision* about whether to implement OO frameworks or not in one's R
package.  I suspect I should, but the utility of the package would be aided
by *collections* of objects.  R, however, doesn't seem to implement
collections.

Background: I am writing an R package that will provide a framework for
analyzing structural models of trees (as in trees made of wood, not
statistical trees).  These models are generated from laser scanning
instruments and model fitting algorithms, and hence may have aspects that
are data-heavy.  Furthermore, coputing metrics based on these structures can
be computationally heavy.  Finally, as a result, each tree has a number of
metrics associated with it (which may be expensive to calculate), along with
the underlying data of that tree.  It will be important as well to perform
calculations across many of these trees, as one would do in a dataframe.

This last point is important: if one organizes data across potentially
thousands of objects, how easy or hard is it to massage properties of those
objects into a dataframe for analysis?

Thank you in advance for thoughts and pointers.

Allie