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