Introduction | Preparation | Content masking | Data labelling | Vectorisation | Validation | Authorship analysis | Analysis of $Q$ | Qualitative examination of evidence | Conclusions | Acknowledgements | References
Package: idiolect 1.2.0.9000
idiolect: Forensic Authorship Analysis
Carry out comparative authorship analysis of disputed and undisputed texts within the Likelihood Ratio Framework for expressing evidence in forensic science. This package contains implementations of well-known algorithms for comparative authorship analysis, such as Smith and Aldridge's (2011) Cosine Delta <doi:10.1080/09296174.2011.533591> or Koppel and Winter's (2014) Impostors Method <doi:10.1002/asi.22954>, as well as functions to measure their performance and to calibrate their outputs into Log-Likelihood Ratios.
Authors:
idiolect_1.2.0.9000.tar.gz
idiolect_1.2.0.9000.zip(r-4.7)idiolect_1.2.0.9000.zip(r-4.6)idiolect_1.2.0.9000.zip(r-4.5)
idiolect_1.2.0.9000.tgz(r-4.6-any)idiolect_1.2.0.9000.tgz(r-4.5-any)
idiolect_1.2.0.9000.tar.gz(r-4.7-any)idiolect_1.2.0.9000.tar.gz(r-4.6-any)
idiolect_1.2.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
idiolect/json (API)
| # Install 'idiolect' in R: |
| install.packages('idiolect', repos = c('https://andreanini.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/andreanini/idiolect/issues
Pkgdown/docs site:https://andreanini.github.io
Datasets:
- enron.sample - Enron sample
Last updated from:0b3fcb2014. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 307 | ||
| source / vignettes | OK | 232 | ||
| linux-release-x86_64 | OK | 291 | ||
| macos-release-arm64 | OK | 291 | ||
| macos-oldrel-arm64 | OK | 216 | ||
| windows-devel | OK | 287 | ||
| windows-release | OK | 292 | ||
| windows-oldrel | OK | 270 | ||
| wasm-release | OK | 129 |
Exports:calibrate_LLRchunk_textsconcordancecontentmaskcreate_corpusdeltadensity_plotimpostorslambdaGlambdaG_visualizemost_similarngram_tracingperformanceposteriortokenize_sentsvectorize
Dependencies:caretclasscliclockcodetoolscpp11data.tablediagramdigestdplyre1071farverfastmatchfdrtoolforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhathereipredisobandISOcodesiteratorsjsonliteKernSmoothkgramslabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixModelMetricsnlmennetnsyllablenumDerivparallellypbapplypillarpkgconfigplyrpngpROCprodlimprogressrproxyproxyCpurrrquantedaquanteda.textstatsR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppProgressRcppTOMLRdpackrecipesreshape2reticulaterlangrpartrprojrootS7scalesshapeSnowballCspacyrsparsevctrsSQUAREMstopwordsstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithrxml2yaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Calibrate scores into Log-Likelihood Ratios | calibrate_LLR |
| Chunk a dataset | chunk_texts |
| Qualitative examination of evidence | concordance |
| Content masking | contentmask |
| Create a corpus | create_corpus |
| Delta | delta |
| Plot density of TRUE/FALSE distributions | density_plot |
| Enron sample | enron.sample |
| Impostors Method | impostors |
| Apply the LambdaG algorithm | lambdaG |
| Visualize the output of the LambdaG algorithm | lambdaG_visualize |
| Select the most similar texts to a specific text | most_similar |
| N-gram tracing | ngram_tracing |
| Performance evaluation | performance |
| Posterior prosecution probabilities and odds | posterior |
| Tokenize to sentences | tokenize_sents |
| Vectorize data | vectorize |
