Package: easy.glmnet 1.0
easy.glmnet: Functions to Simplify the Use of 'glmnet' for Machine Learning
Provides several functions to simplify using the 'glmnet' package: converting data frames into matrices ready for 'glmnet'; b) imputing missing variables multiple times; c) fitting and applying prediction models straightforwardly; d) assigning observations to folds in a balanced way; e) cross-validate the models; f) selecting the most representative model across imputations and folds; and g) getting the relevance of the model regressors; as described in several publications: Solanes et al. (2022) <doi:10.1038/s41537-022-00309-w>, Palau et al. (2023) <doi:10.1016/j.rpsm.2023.01.001>, Sobregrau et al. (2024) <doi:10.1016/j.jpsychores.2024.111656>.
Authors:
easy.glmnet_1.0.tar.gz
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easy.glmnet.pdf |easy.glmnet.html✨
easy.glmnet/json (API)
| # Install 'easy.glmnet' in R: |
| install.packages('easy.glmnet', repos = c('https://jrad1.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 6 days agofrom:bd462a6888. Checks:OK: 1 NOTE: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 12 2024 |
| R-4.5-win | NOTE | Sep 12 2024 |
| R-4.5-linux | NOTE | Sep 12 2024 |
| R-4.4-win | NOTE | Sep 12 2024 |
| R-4.4-mac | NOTE | Sep 12 2024 |
| R-4.3-win | NOTE | Sep 12 2024 |
| R-4.3-mac | NOTE | Sep 12 2024 |
Exports:assign.foldscvdata.frame2glmnet.matrixdata.frame2glmnet.matrix_fitglmnet_fitglmnet_get.items.relevanceglmnet_get.main.modelglmnet_predictimpute.glmnet.matriximpute.glmnet.matrix_fitsurv2binary
Dependencies:codetoolsdoParallelforeachglmnetiteratorslatticeMatrixRcppRcppEigenshapesurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Assign observations to folds in a balanced way | assign.folds |
| Conduct cross-validation | cv |
| Convert a data.frame into a matrix ready for glmnet | data.frame2glmnet.matrix data.frame2glmnet.matrix_fit |
| Obtain and use a glmnet prediction model | glmnet_fit glmnet_predict |
| Get the relevance of the model items | glmnet_get.items.relevance |
| Get the main glmnet model across imputations and folds | glmnet_get.main.model |
| Impute missing variables in a glmnet matrix multiple times | impute.glmnet.matrix impute.glmnet.matrix_fit |
| Convert a "Surv" object into binary variables at different time points | surv2binary |
