Package: LLMing 1.2.1
LLMing: Large Language Model (LLM) Tools for Psychological Text Analysis
A collection of large language model (LLM) text analysis methods designed with psychological data in mind. Currently, LLMing (aka "lemming") includes a text anomaly detection method based on the angle-based subspace approach described by Zhang, Lin, and Karim (2015) and a text generation method. <doi:10.1016/j.ress.2015.05.025>.
Authors:
LLMing_1.2.1.tar.gz
LLMing_1.2.1.zip(r-4.7-any)LLMing_1.2.1.zip(r-4.6-any)LLMing_1.2.1.zip(r-4.5-any)
LLMing_1.2.1.tgz(r-4.6-any)LLMing_1.2.1.tgz(r-4.5-any)
LLMing_1.2.1.tar.gz(r-4.7-any)LLMing_1.2.1.tar.gz(r-4.6-any)
LLMing_1.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
LLMing/json (API)
| # Install 'LLMing' in R: |
| install.packages('LLMing', repos = c('https://sliplr19.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/sliplr19/llming/issues
Last updated from:66c379c6ff. Checks:7 ERROR, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | ERROR | 173 | ||
| source / vignettes | OK | 219 | ||
| linux-release | ERROR | 169 | ||
| macos-release | ERROR | 153 | ||
| macos-oldrel | ERROR | 122 | ||
| windows-devel | ERROR | 74 | ||
| windows-release | ERROR | 73 | ||
| windows-oldrel | ERROR | 119 | ||
| wasm-release | OK | 176 |
Exports:embedG_thresnormahalopCOSpCOS_rowrep_setsim_SNNtext_datagentextanomalyvector_SNNz_score
Dependencies:base64encbitbit64bslibcachemclassclicliprclockcodetoolscolorspacecommonmarkcowplotcpp11crayoncurldata.tabledbscandiagramdialsDiceDesigndigestdplyrevaluatefarverfastmapfastmatchfloatfontawesomefsfurrrfuturefuture.applyGauProgenericsggforceggplot2ggrepelggwordcloudglobalsgluegowergridtextgtablegtoolshardhatherehighrhmshtmltoolsipredisobandISOcodesjpegjquerylibjsonliteKernSmoothknitrlabelinglatticelavalbfgslgrlifecyclelistenvlitedownlubridatemagrittrmarkdownMASSMatrixMatrixExtramemoisemimemixoptmlapimodelenvnnetnumDerivparallellyparsnippatchworkpillarpkgconfigpngpolyclippracmaprettyunitsprodlimprogressprogressrpurrrquantedaR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppProgressRcppTOMLRdpackreadrrecipesreticulateRhpcBLASctlrlangrmarkdownrpartrprojrootrsamplersparseRSpectraS7sassscalessfdshapesliderSnowballCsparsevctrssplitfngrSQUAREMstopwordsstringistringrsurvivalsystemfontstailortexttext2vectextmineRtibbletidyrtidyselecttimechangetimeDatetinytextopicstunetweenrtzdbutf8vctrsviridisLitevroomwarpwithrworkflowsxfunxml2yamlyardstick
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Embed texts with a selected embedding model | embed |
| Thresholding of pCOS dataframe | G_thres |
| Local outlier score | normahalo |
| pCOS scores for every row of dataframe | pCOS |
| Pairwise cosine-style row score | pCOS_row |
| The vectors of the shared nearest neighbors | rep_set |
| Compute shared nearest neighbors | sim_SNN |
| Generate text data via Python LLM | text_datagen |
| Text anomaly score | textanomaly |
| Aggregate dataframe into mean feature vectors Aggregrate dataframe into mean feature vectors | vector_SNN |
| Z-score on columns | z_score |
