Package: glmtrans 2.0.0
glmtrans: Transfer Learning under Regularized Generalized Linear Models
We provide an efficient implementation for two-step multi-source transfer learning algorithms in high-dimensional generalized linear models (GLMs). The elastic-net penalized GLM with three popular families, including linear, logistic and Poisson regression models, can be fitted. To avoid negative transfer, a transferable source detection algorithm is proposed. We also provides visualization for the transferable source detection results. The relevant paper is available on arXiv: <arxiv:2105.14328>.
Authors:
glmtrans_2.0.0.tar.gz
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glmtrans.pdf |glmtrans.html✨
glmtrans/json (API)
| # Install 'glmtrans' in R: |
| install.packages('glmtrans', repos = c('https://ytstat.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:27cfd76926. Checks:OK: 1 WARNING: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 10 2024 |
| R-4.5-win | WARNING | Sep 10 2024 |
| R-4.5-linux | WARNING | Sep 10 2024 |
| R-4.4-win | WARNING | Sep 10 2024 |
| R-4.4-mac | WARNING | Sep 10 2024 |
| R-4.3-win | WARNING | Sep 10 2024 |
| R-4.3-mac | WARNING | Sep 10 2024 |
Exports:glmtransglmtrans_infmodelssource_detection
Dependencies:assertthatcaretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdoParalleldplyre1071fansifarverforeachformatRfuturefuture.applygenericsggplot2glmnetglobalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppEigenrecipesreshape2rlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Fit a transfer learning generalized linear model (GLM) with elasticnet regularization. | glmtrans |
| Calculate asymptotic confidence intervals based on desparsified Lasso and two-step transfer learning method. | glmtrans_inf |
| Generate data from Gaussian, logistic and Poisson models. | models |
| Visualize the losses of different sources and the threshold to determine transferability. | plot.glmtrans plot.glmtrans_source_detection |
| Predict for new data from a "glmtrans" object. | predict.glmtrans |
| Print a fitted "glmtrans" object. | print.glmtrans |
| Transferable source detection for GLM transfer learning algorithm. | source_detection |
