Package: viralx 1.3.1
Juan Pablo Acuña González
viralx: Explainers for Regression Models in HIV Research
A dedicated viral-explainer model tool designed to empower researchers in the field of HIV research, particularly in viral load and CD4 (Cluster of Differentiation 4) lymphocytes regression modeling. Drawing inspiration from the 'tidymodels' framework for rigorous model building of Max Kuhn and Hadley Wickham (2020) <https://www.tidymodels.org>, and the 'DALEXtra' tool for explainability by Przemyslaw Biecek (2020) <doi:10.48550/arXiv.2009.13248>. It aims to facilitate interpretable and reproducible research in biostatistics and computational biology for the benefit of understanding HIV dynamics.
Authors:
viralx_1.3.1.tar.gz
viralx_1.3.1.zip(r-4.7-any)viralx_1.3.1.zip(r-4.6-any)viralx_1.3.1.zip(r-4.5-any)
viralx_1.3.1.tgz(r-4.6-any)viralx_1.3.1.tgz(r-4.5-any)
viralx_1.3.1.tar.gz(r-4.7-any)viralx_1.3.1.tar.gz(r-4.6-any)
viralx_1.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
viralx/json (API)
| # Install 'viralx' in R: |
| install.packages('viralx', repos = c('https://juanv66x.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/juanv66x/viralx/issues
- train2 - Training Data for Explainability of Models
Last updated from:ef4ea19f40. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 193 | ||
| source / vignettes | OK | 175 | ||
| linux-release | OK | 189 | ||
| macos-release | OK | 182 | ||
| macos-oldrel | OK | 160 | ||
| windows-devel | OK | 92 | ||
| windows-release | OK | 94 | ||
| windows-oldrel | OK | 76 | ||
| wasm-release | OK | 104 |
Exports:glob_cr_visglob_knn_visglob_nn_visviralx_knnviralx_knn_globviralx_knn_shapviralx_knn_visviralx_marsviralx_mars_shapviralx_mars_visviralx_nnviralx_nn_globviralx_nn_shapviralx_nn_vis
Dependencies:classcliclockcodetoolscpp11DALEXDALEXtradata.tablediagramdigestdoFuturedplyrfarverforeachfuturefuture.applygenericsggplot2globalsgluegowergridExtragtablehardhatiBreakDowningredientsipredisobanditeratorskernelshapKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmodelenvnnetnumDerivparallellyparsnippillarpkgconfigprettyunitsprodlimprogressrpurrrR6RColorBrewerRcpprecipesrlangrpartS7scalesshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithrworkflows
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Global Visualization of SHAP Values for Cubist Rules Model | glob_cr_vis |
| Global Visualization of SHAP Values for K-Nearest Neighbor Model | glob_knn_vis |
| Global Visualization of SHAP Values for Neural Network Model | glob_nn_vis |
| Training Data for Explainability of Models | train2 |
| Explain K-Nearest Neighbors Model | viralx_knn |
| Global Explainers for K-Nearest Neighbor Models | viralx_knn_glob |
| Explain K Nearest Neighbor Model using SHAP values | viralx_knn_shap |
| Visualize SHAP Values for K-Nearest Neighbor Model | viralx_knn_vis |
| Explain Multivariate Adaptive Regression Splines Model | viralx_mars |
| Explain Multivariate Adaptive Regression Splines Using SHAP Values | viralx_mars_shap |
| Visualize SHAP Values for Multivariate Adaptive Regression Splines Model | viralx_mars_vis |
| Explain Neural Network Regression Model | viralx_nn |
| Global Explainers for Neural Network Models | viralx_nn_glob |
| Explain Neural Network Model Using SHAP Values | viralx_nn_shap |
| Visualize SHAP Values for Neural Network Model | viralx_nn_vis |