Urgent:
-------
* add to all functions using data.frame[idx, ] --> data.frame[idx, , drop=FALSE]
  ## this will return single column data.frame objects as data.frames, instead of coercing to vectors

* ADD to all modules using data.frame, melt, ... ?
  ## important: change the default behavior of data.frame and melt
  opt.original <- options(stringsAsFactors = FALSE)

* determine optimal level of checking in validity method for SoilProfileCollection objects

* make slab() faster / more efficient (data.table ? )

* slab() no longer supports weighted-aggregates

* more aggressive checking of ordering / IDs --> use joins instead of implicit ordering

* switch to reshape2? It is faster (PR) [code-fixes required in slab() before we can do this (DEB)]

* S4-cleanup of profile_compare [this would solve several issues related to ordering]

* raster::extract method

* sp::over method

* rgdal::spTransform method


Interesting:
------------
1. user-defined slices for profile_compare()
2. sensitivity analysis on profile_compare()
3. equal-area splines


Optimisation of slab()
-----------------------
0. investigate use of data.table objects instead of data.frame objects
1. investigate .slab.fun.numeric.fast() vs. .slab.fun.numeric.default() for large data sets
2. ideas:

	http://jeromyanglim.blogspot.com/2010/02/case-study-in-optimising-code-in-r.html
	http://www.noamross.net/blog/2013/4/25/faster-talk.html
	https://gist.github.com/noamross/5447008

3. Rprof results:

PctTime Stack                                                                                                                                                           
 19.160  slab > standardGeneric > slab > aggregate > aggregate.formula > aggregate.data.frame > lapply > FUN > lapply > split > split.default > factor                   
 15.350  slab > standardGeneric > slab > aggregate > aggregate.formula > aggregate.data.frame > lapply > FUN > lapply > FUN > hdquantile > outer > FUN > pbeta           
 13.064  slab > standardGeneric > slab > slice > standardGeneric > slice > ldply > list_to_dataframe > rbind.fill                                                        
  4.293  slab > standardGeneric > slab > aggregate > aggregate.formula > eval > eval > model.frame > model.frame.default > sapply > lapply > as.list > as.list.data.frame
  4.256  slab > standardGeneric > slab > melt > melt.data.frame > do.call > lapply > FUN > data.frame                                                                    
  3.977  slab > standardGeneric > slab > join > .join_all > cbind > cbind > data.frame                                                                                   
  2.918  slab > standardGeneric > slab > slice > standardGeneric > slice > join > .join_first > join.keys > id > id_var > sort > sort.default > sort.int                 
  2.639  slab > standardGeneric > slab > melt > melt.data.frame > do.call > rbind > rbind                                                                                
  2.602  slab > standardGeneric > slab > aggregate > aggregate.formula > aggregate.data.frame                                                                            
  2.026  slab > standardGeneric > slab > join > .join_all > cbind > [ > [.data.frame > make.unique                                                                       
  1.896  slab > standardGeneric > slab > slice > standardGeneric > slice > get.slice > apply > FUN > which                                                               
  1.821  slab > standardGeneric > slab > aggregate > aggregate.formula > aggregate.data.frame > is.data.frame > [ > [.data.frame > structure                             
  1.635  slab > standardGeneric > slab > melt > melt.data.frame > [[<- > [[<-.data.frame                                                                                 
  1.096  slab > standardGeneric > slab > melt > melt.data.frame                                                                                                          
  0.966  slab > standardGeneric > slab > aggregate > aggregate.formula > aggregate.data.frame > lapply > FUN > lapply > FUN > hdquantile > outer                         
  0.911  slab > standardGeneric > slab > aggregate > aggregate.formula > aggregate.data.frame > as.factor > factor > unique > unique.default                             
  0.873  slab > standardGeneric > slab > slice > standardGeneric > slice > [ > [.data.frame > order                                                                      
  0.799  slab > standardGeneric > slab > slice > standardGeneric > slice > get.slice > apply > FUN                                                                       
  0.688  slab > standardGeneric > slab > aggregate > aggregate.formula > aggregate.data.frame > lapply > FUN > lapply > FUN > hdquantile                                 
  0.539  slab > standardGeneric > slab > join > .join_all > join_ids > vapply 

Total Time: 107.62 seconds