TODO

-------------- Short term --------------------

- bug on `[` for SDF. At the moment needs to specify k=350:500 (for example). We need to be able to do this without specifying the option name

- Re-write DESCRIPTION file, esp. Imports/Suggests part
- implement "[<-" (like australia[,'PC1',] <- NULL) 
- order of rows, cols, spectra in `[` --> use rows, spectra, cols
- implement how to delete features from @data slot
- moving average/moving window stuff? --> linear interpolation?
- Matrix operations (maybe this could just be done by using spectra(foo)?)
- sweep() would be good as well
- implement parallel option throught something like `inspectr_options(...)`
- rename continuum_removal to something shorter? cont_rm?
- improve fill_spectra? Solution may as well be including a drop=... option in cut() and `[`. When TRUE, removes WL, when FALSE just puts NA values. Consequences for further modelling?
- implement (spatial) overlay function

- Begin to work on vignettes:
  * introduction vignette --> Creation and maniuplation, basic stuff like PCA, k-means and hclust
  * analysis vignette
  * graphics vignette
  * spatial vignette --> PC study, raster overlays
  * hacking with inspectr: how and when to hook your own 
    functions in, how to work with other packages
  * soil vignette

- Push the SpatialSpectraDataFrame class

- allow rbind to work on Spectra + SpectraDataFrames (including NA for Spectra individuals)
- what about cbind/strategy to add wavelengths to a collection?

- Implement a sample_spectra.Spectra() method, including various spectra sampling strategies:
  * random
  * KS
  * OptiSim
  * K-means
  * DBSCAN
  * ref: http://dx.doi.org/10.1016/S0003-2670(02)00651-7

- Push the Kennard-Stone and OptiSim algorithms for sampling
- Use RCpp(Armadillo?) for the KS algo

- outlier analysis methods (Mahalonobis?) --> Just an illustration in the vignette, use package:chemometrics
- 2D correlation between two sets of spectra --> same

- Document how to centre the spectra --> apply_spectra(foo, scale)
- Implement wavelet-based wl selection
- Implement the parameter orthogonalisation algorithm (Roger et al., 2003)
- conversion to and from hyperSpec
- Re-do Viscarra-Rossel & Behrens (2010) with inspectr
- Showcase the use of Cubist models

- Potential speed up for reading big files: sqldf::sqldf
I could implement a file=... option in spectra() to create Spectra
object directly from the file (provided there's a formula)
Here's the relevant code snippet:
rd_sqldf <- function(fn) {
  con <- file(fn)
  sqldf("select * from con", dbname = tempfile(), file.format = list(header = T, row.names = F))
}

------------- Longer term --------------------

- Identify slow stuff and see if they can be done in C++ (eg advanced sampling methods)
- File-based storage of big collections
- data.table?
- Integration of a viewer, possibly using playwith, to
manipulate (eg crop) the spectra in a collection.