Package: XAItest 0.99.25
Ghislain FIEVET
XAItest: XAItest: Enhancing Feature Discovery with eXplainable AI
XAItest is an R Package that identifies features using eXplainable AI (XAI) methods such as SHAP or LIME. This package allows users to compare these methods with traditional statistical tests like t-tests, empirical Bayes, and Fisher's test. Additionally, it includes a system that enables the comparison of feature importance with p-values by incorporating calibrated simulated data.
Authors:
XAItest_0.99.25.tar.gz
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XAItest.pdf |XAItest.html✨
XAItest/json (API)
NEWS
| # Install 'XAItest' in R: |
| install.packages('XAItest', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/ghislainfievet/xaitest/issues
On BioConductor:XAItest-0.99.24(bioc 3.21)
softwarestatisticalmethodfeatureextractionclassificationregression
Last updated 2 days agofrom:8d95c176b3. Checks:1 ERROR, 8 WARNING. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | FAIL | Mar 24 2025 |
| R-4.5-win | WARNING | Mar 24 2025 |
| R-4.5-mac | WARNING | Mar 24 2025 |
| R-4.5-linux | WARNING | Mar 24 2025 |
| R-4.4-win | WARNING | Mar 24 2025 |
| R-4.4-mac | WARNING | Mar 24 2025 |
| R-4.4-linux | WARNING | Mar 24 2025 |
| R-4.3-win | WARNING | Mar 24 2025 |
| R-4.3-mac | WARNING | Mar 24 2025 |
Exports:getFeatImpThresholdsgetMetricsTablemapPvalImportancemodelsOverviewplotModelsetMetricsTableXAI.test
Dependencies:abindaskpassassertthatbase64encBiobaseBiocGenericsbslibcachemcaretclasscliclockcodetoolscolorspacecpp11crayoncrosstalkcurldata.tableDelayedArraydiagramdigestdplyrDTe1071evaluatefansifarverfastmapfontawesomeforeachfsfuturefuture.applygenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2glmnetglobalsgluegowergtablehardhathighrhtmltoolshtmlwidgetshttpuvhttripredIRangesisobanditeratorsjquerylibjsonlitekernelshapKernSmoothknitrlabelinglaterlatticelavalazyevallifecyclelimelimmalistenvlubridatemagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemgcvmimeModelMetricsmunsellnlmennetnumDerivopensslparallellypillarpkgconfigplyrpROCprodlimprogressrpromisesproxypurrrR6randomForestrappdirsRColorBrewerRcppRcppEigenrecipesreshape2rlangrmarkdownrpartS4ArraysS4VectorssassscalesshapeSparseArraysparsevctrsSQUAREMstatmodstringistringrSummarizedExperimentsurvivalsystibbletidyrtidyselecttimechangetimeDatetinytextzdbUCSC.utilsutf8vctrsviridisLitewithrxfunXVectoryaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| The getFeatImpThresholds function identifies the minimum level of feature importance required to exceed a specified significance threshold, which is determined by the p-value. | getFeatImpThresholds |
| Get the Metrics Table | getMetricsTable getMetricsTable,ObjXAI-method |
| The mapPvalImportance function displays a datatable with color-coded cells based on significance thresholds for feature importance and p-value columns. | mapPvalImportance |
| Models Overview | modelsOverview |
| ObjXAI class | .objXAI ObjXAI-class |
| Plot the model | plotModel |
| Set the Metrics Table | setMetricsTable setMetricsTable,ObjXAI-method |
| Show Method for ObjXAI | show,ObjXAI-method |
| The XAI.test function complements t-test and correlation analyses in feature discovery by integrating eXplainable AI techniques such as feature importance, SHAP, LIME, or custom functions. It provides the option of automatic integration of simulated data to facilitate matching significance between p-values and feature importance. | XAI.test |
