Provides shortcuts in extracting useful data points and summarizing waveform data. It is optimized for speed to work efficiently with large data sets so you can get to the analysis phase more quickly. It also utilizes a user-friendly format for use by both beginners and seasoned R users.

Aligns peak based on peak retention times and matches homologous peaks
    across samples. The underlying alignment procedure comprises three sequential steps.
    (1) Full alignment of samples by linear transformation of retention times to 
    maximise similarity among homologous peaks (2) Partial alignment of peaks within 
    a user-defined retention time window to cluster homologous peaks (3) Merging rows
    that are likely representing homologous substances (i.e. no sample shows peaks in 
    both rows and the rows have similar retention time means). The algorithm is described in detail
    in Ottensmann et al., 2018 <doi:10.1371/journal.pone.0198311>.

These are two-sample tests for categorical data utilizing similarity information among the categories.  They are useful when there is underlying structure on the categories.

'GPU'/CPU Benchmarking on Debian-package based systems
 This package benchmarks performance of a few standard linear algebra
 operations (such as a matrix product and QR, SVD and LU decompositions)
 across a number of different 'BLAS' libraries as well as a 'GPU' implementation.
 To do so, it takes advantage of the ability to 'plug and play' different
 'BLAS' implementations easily on a Debian and/or Ubuntu system.  The current
 version supports
  - 'Reference BLAS' ('refblas') which are un-accelerated as a baseline
  - Atlas which are tuned but typically configure single-threaded
  - Atlas39 which are tuned and configured for multi-threaded mode
  - 'Goto Blas' which are accelerated and multi-threaded
  - 'Intel MKL' which is a commercial accelerated and multithreaded version.
 As for 'GPU' computing, we use the CRAN package
  - 'gputools'
 For 'Goto Blas', the 'gotoblas2-helper' script from the ISM in Tokyo can be
 used. For 'Intel MKL' we use the Revolution R packages from Ubuntu 9.10.