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NEWS.md

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Changes in v1.4.2

  • Add as.textmodel_lss() for objects from the wordvector package.
  • Reduce dependent packages by moving rsparse, irlba and rsvd to Suggests.
  • Fix handling of phrasal patterns in textplot_terms().
  • Improve objects created by as.textmodel_lss.textmodel_lss().

Changes in v1.4.1

  • Add group to smooth_lss() to smooth LSS scores by group.
  • Add optimize_lss() as an experimental function.

Changes in v1.4.0

  • Change the default value to max_highlighted = 1000 in textplot_terms().
  • Add ... to customize text labels to textplot_terms().
  • Highlight words in different colors when a dictionary is passed to highlighted.
  • Add mode = "predict" and remove = FALSE to bootstrap_lss().

Changes in v1.3.2

  • Fix the error in textplot_terms() when the frequency of terms are zero (#85).

Changes in v1.3.1

  • Fix the range of scores when cut is used.
  • Add bootstrap_lss() as an experimental function.

Changes in v1.3.0

  • Add cut to predict.
  • Move examples to the new package website: http://koheiw.github.io/LSX.
  • Rename "rescaling" to "rescale" for simplicity and consistency.
  • Improve random sampling of words to highlight in textplot_terms() to avoid congestion.

Changes in v1.2.0

  • Add group_data to textmodel_lss() to simplify the workflow.
  • Add max_highlighted to textplot_terms() to automatically highlight polarity words.

Changes in v1.1.4

  • Update as.textmodel_lss() to avoid errors in textplot_terms() when terms is used.

Changes in v1.1.3

  • Restore examples for textmodel_lss().
  • Defunct char_keyness() that has been deprecated for long.

Changes in v1.1.2

  • Update examples to pass CRAN tests.

Changes in v1.1.1

  • Add min_n to predict() to make polarity scores of short documents more stable.

Changes in v1.1.0

  • Add as.textmodel_lss() for textmodel_lss objects to allow modifying existing models.
  • Allow terms in textmodel_lss() to be a named numeric vector to give arbitrary weights.

Changes in v1.0.2

  • Add the auto_weight argument to textmodel_lss() and as.textmodel_lss() to improve the accuracy of scaling.
  • Remove the group argument from textplot_simil() to simplify the object.
  • Make as.seedwords() to accept multiple indices for upper and lower.

Changes in v1.0.0

  • Add max_count to textmodel_lss.fcm() that will be passed to x_max in rsparse::GloVe$new().
  • Add max_words to textplot_terms() to avoid overcrowding.
  • Make textplot_terms() to work with objects from textmodel_lss.fcm().
  • Add concatenator to as.seedwords().

Changes in v0.9.9

  • Correct how textstat_context() and char_context() computes statistics.
  • Deprecate char_keyness().

Changes in v0.9.8

  • Stop using functions and arguments deprecated in quanteda v3.0.0.

Changes in v0.9.7

  • Make as.textmodel_lss.matrix() more reliable.
  • Remove quanteda.textplots from dependencies.

Changes in v0.9.6

  • Updated to reflect changes in quanteda (creation of quanteda.textstats).

Changes in v0.9.4

  • Fix char_context() to always return more frequent words in context.
  • Experimental textplot_factor() has been removed.
  • as.textmodel_lss() takes a pre-trained word-embedding.

Changes in v0.9.3

  • Add textstat_context() and char_context() to replace char_keyness().
  • Make the absolute sum of seed weight equal to 1.0 in both upper and lower ends.
  • textplot_terms() takes glob patterns in character vector or a dictionary object.
  • char_keyness() no longer raise error when no patter is found in tokens object.
  • Add engine to smooth_lss() to apply locfit() to large datasets.

Changes in v0.9.2

  • Updated unit tests for the new versions of stringi and quanteda.

Changes in v0.9.0

  • Renamed from LSS to LSX for CRAN submission.

Changes in v0.8.7

  • Added textplot_terms() to improve visualization of model terms.