tlda0.1.0 package

Tools for Language Data Analysis

disp_DA_tdm

Calculate the dispersion measure DAD_{A} for a term-document matrix

disp_DA

Calculate the dispersion measure DAD_{A}

disp_DKL_tdm

Calculate the dispersion measure DKLD_{KL} for a term-document matrix

disp_DKL

Calculate the dispersion measure DKLD_{KL}

disp_DP_tdm

Calculate Gries's deviation of proportions for a term-document mat...

disp_DP

Calculate Gries's deviation of proportions

disp_R_tdm

Calculate the dispersion measure 'range' for a term-document matrix

disp_R

Calculate the dispersion measure 'range'

disp_S_tdm

Calculate the dispersion measure SS for a term-document matrix

disp_S

Calculate the dispersion measure SS

disp_tdm

Calculate parts-based dispersion measures for a term-document matrix

disp

Calculate parts-based dispersion measures

find_max_disp_tdm

Find the maximally dispersed distribution of each item in a term-docum...

find_max_disp

Find the maximally dispersed distribution of an item across corpus par...

find_min_disp_tdm

Find the minimally dispersed distribution of each item in a term-docum...

find_min_disp

Find the minimally dispersed distribution of an item across corpus par...

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Support functions and datasets to facilitate the analysis of linguistic data. The current focus is on the calculation of corpus-linguistic dispersion measures as described in Gries (2021) <doi:10.1007/978-3-030-46216-1_5> and Soenning (2025) <doi:10.3366/cor.2025.0326>. The most commonly used parts-based indices are implemented, including different formulas and modifications that are found in the literature, with the additional option to obtain frequency-adjusted scores. Dispersion scores can be computed based on individual count variables or a term-document matrix.

  • Maintainer: Lukas Soenning
  • License: MIT + file LICENSE
  • Last published: 2025-04-25

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