Version 0.2-0:

  - the first public release of the wordspace package \o/

Version 0.1-24:

  - read.dsm.triplet can now load marginal frequencies from separate files, so it is a full-fledged replacement for read.dsm.ucs (though less memory-efficient than the native UCS format for very large models)
  - RC 2 for public release

Version 0.1-23:

  - completed vignette with tutorial introduction
  - RC 1 for public release

Version 0.1-22:

  - clean up & complete documentation in preparation for first public release
  - reduce size of example data (verb-noun triples, pre-compiled DSM vectors)

Version 0.1-21:

  - replace readr::read_delim() with iotools::read.delim.raw(), which is slightly faster and leaner; also avoids expensive dependencies of readr such as BH (Boost libraries)
  - implemented work-arounds to support compressed files and different character encodings with iotools
  - package test for file input (triplet and UCS format, different encodings) with suitable sample files in extdata/

Version 0.1-20:

  - new sample data: DSM objects for small illustrative term-term and term-context matrix

Version 0.1-19:

  - complete basic documentation for all functions and data sets
  - data set DSM_VerbNounTriples_DESC removed to reduce package size
  - dsm.projection() now supports power-scaling for SVD-based projection methods

Version 0.1-18:

  - efficient truncated SVD of sparse matrix using SVDLIBC code from 'sparsesvd' package
  - faster reading of triplet files with 'readr' package (though not very memory-efficient)

Version 0.1-17:

  - Minkowski distance and length measures generalized to 0 <= p < 1 (but not homogeneous for p < 1, hence not a proper mathematical norm)

Version 0.1-16:

  - plot() method for dist.matrix for easy visualization of neighbourhood graphs
  - head() methods to extract top left corner of DSM object (dsm) or distance matrix (dist.matrix)
  - print() method for DSM objects, so users don't accidentally print a large co-occurrence matrix
  - new sample data set: DSM_Vectors with 100-dimensional pre-compiled representations for selected words
  - new sample data: typical singular values from term-context matrix
  - new sample data: "goods" example illustrating dimensionality reduction based on correlations

Version 0.1-14:

  - new evaluation task: SemCorWSD (preliminary version)
  - CITATION entry with official reference (Evert 2014)
  - enhanced functionality in nearest.neighbours(): support for cross-distance setting, targets can be given as vectors or by name, neighbour search in pre-computed distance or similarity matrix,  optionally return distance matrix for target and its neighbours

Version 0.1-13:

  - Rcpp implementation of scaleMargins() further reduces memory overhead (with in-place operation for internal use)
  - as.dsm() method converts term-document and document-term matrices from tm package into DSM objects
  - added support functions for evaluation of DSMs in standard tasks (multiple choice, similarity correlation and clustering)
  - new sample data sets: tables of verb-noun cooccurrences from BNC and DESC corpora
  - new evaluation tasks: RG65, WordSim353, ESSLLI08_Nouns

Version 0.1-10:

  - use Rcpp instead of deprecated .C() native code interface
  - for performance reasons, .C() was used with DUP=FALSE, which is no longer allowed as of R 3.1.0
  - in addition, some package tests for dsm.score(), dist.matrix() and dsm.projection() were added
  - the package now depends on Rcpp (>= 0.11.0) and R (>= 3.0.0)

Version 0.1:

  - partial re-design of DSM objects and basic functions
  - handling of sparse and non-negative co-occurrence matrices has been re-thought
  - not fully compatible with v0.0 series (but basic usage should not be affected)
    
Version 0.0-25:

  - randomized SVD available as separate function rsvd()

Version 0.0-24

  - OpenMP no longer activated by default
  - wordspace.openmp() to check for OpenMP support and select the number of parallel threads

Version 0.0-23

  - further performance improvements
  - dist.matrix() uses less memory and is considerably faster for cosine/angle distance measure
  - new function pair.distances() computes distances or neighbour ranks for a list of word pairs efficiently
  - nearest.neighbours() automatically processes a long list of lookup terms in moderately sized batches

Version 0.0-21:

 - experimental support for OpenMP on appropriate platforms
 - n/a on Mac OS X in the default R installation (but achieves speed-up if expressly activated)
 - parallelization only used if more than 100 M operations have to be carried out (purely heuristic limit)
 - first experiments suggests that using more than 4 or 8 threads brings little benefit with enormous overhead
 - setting OMP_NUM_THREADS is strongly recommended but may also affect BLAS matrix operations (e.g. with OpenBLAS)

