I have a R data.frame containing longitude, latitude which spans over the entire USA map.  When X number of entries are all within a small geographic region of say a few degrees longitude & a few degrees latitude, I want to be able to detect this and then have my program then return the coordinates for the geographic bounding box.  Is there a Python or R CRAN package that already does this? If not, how would I go about ascertaining this information?
A few ideas:

Ad-hoc & approximate: The "2-D histogram". Create arbitrary "rectangular" bins, of the degree width of your choice, assign each bin an ID.  Placing a point in a bin means "associate the point with the ID of the bin".  Upon each add to a bin, ask the bin how many points it has.  Downside: doesn't correctly "see" a cluster of points that stradle a bin boundary; and: bins of "constant longitudinal width" actually are (spatially) smaller as you move north.
Use the "Shapely" library for Python.  Follow it's stock example for "buffering points", and do a cascaded union of the buffers.  Look for globs over a certain area, or that "contain" a certain number of original points.  Note that Shapely is not intrinsically "geo-savy", so you'll have to add corrections if you need them.
Use a true DB with spatial processing.  MySQL, Oracle, Postgres (with PostGIS), MSSQL all (I think) have "Geometry" and "Geography" datatypes, and you can do spatial queries on them (from your Python scripts). 

Each of these has different costs in dollars and time (in the learning curve)... and different degrees of geospatial accuracy.  You have to pick what suits your budget and/or requirements.
Detecting geographic clusters