Package: SEMdeep 1.0.0
SEMdeep: Structural Equation Modeling with Deep Neural Network and Machine Learning
Training and validation of a custom (or data-driven) Structural Equation Models using layer-wise Deep Neural Networks or node-wise Machine Learning algorithms, which extend the fitting procedures of the 'SEMgraph' R package <doi:10.32614/CRAN.package.SEMgraph>.
Authors:
SEMdeep_1.0.0.tar.gz
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SEMdeep_1.0.0.tgz(r-4.5-any)SEMdeep_1.0.0.tgz(r-4.4-any)SEMdeep_1.0.0.tgz(r-4.3-any)
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SEMdeep.pdf |SEMdeep.html✨
SEMdeep/json (API)
NEWS
| # Install 'SEMdeep' in R: |
| install.packages('SEMdeep', repos = c('https://barbaratarantino.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/barbaratarantino/semdeep/issues
Last updated 2 months agofrom:7d6f50c7d2. Checks:1 OK, 6 NOTE, 2 ERROR. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 27 2025 |
| R-4.5-win | NOTE | Mar 27 2025 |
| R-4.5-mac | NOTE | Mar 27 2025 |
| R-4.5-linux | NOTE | Mar 27 2025 |
| R-4.4-win | NOTE | Mar 27 2025 |
| R-4.4-mac | NOTE | Mar 27 2025 |
| R-4.4-linux | NOTE | Mar 27 2025 |
| R-4.3-win | ERROR | Mar 27 2025 |
| R-4.3-mac | ERROR | Mar 27 2025 |
Exports:classificationReportcrossValidationgetConnectionWeightgetGradientWeightgetShapleyR2getSignificanceTestgetVariableImportancemapGraphnplotpredict.DNNpredict.MLpredict.SEMSEMdnnSEMml
Dependencies:abindaspectbackportsBHBiocGenericsBiocManagerbitbit64bitopsbootcallrcheckmatecherrycitoclassclicodetoolscolorspacecorocorpcorcpp11crayoncurldagittydata.tabledescdoSNOWdplyre1071fansifarverfilelockflipforeachfsgdatagenericsggmggplot2glassoglmnetgluegraphgridExtragtablegtoolshmshommeligraphisobanditeratorsjpegjsonlitekernelshaplabelinglatticelavaanlifecyclelme4lpSolvemagrittrMASSMatrixmgcvminqamnormtmunsellmvtnormNeuralNetToolsnlmenloptrnnetnumDerivparabarpbapplypbivnormpillarpkgconfigplyrpngprettyunitsprocessxprogressprotoclustproxypspurrrquadprogR6rangerrappdirsRBGLrbibutilsRColorBrewerRcppRcppEigenRdpackreformulasreshape2RgraphvizrlangrpartsafetensorsscalesSEMgraphshapesnowsomeMTPstringistringrsurvivaltibbletidyrtidyselecttorchtorchvisionutf8V8vctrsviridisLitewithrxgboost
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Prediction evaluation report of a classification model | classificationReport |
| Cross-validation of linear SEM, ML or DNN training models | crossValidation |
| Connection Weight method for neural network variable importance | getConnectionWeight |
| Gradient Weight method for neural network variable importance | getGradientWeight |
| Compute variable importance using Shapley (R2) values | getShapleyR2 |
| Test for the significance of neural network inputs | getSignificanceTest |
| Variable importance for Machine Learning models | getVariableImportance |
| Map additional variables (nodes) to a graph object | mapGraph |
| Create a plot for a neural network model | nplot |
| SEM-based out-of-sample prediction using layer-wise DNN | predict.DNN |
| SEM-based out-of-sample prediction using node-wise ML | predict.ML |
| SEM-based out-of-sample prediction using layer-wise ordering | predict.SEM |
| Layer-wise SEM train with a Deep Neural Netwok (DNN) | SEMdnn |
| Nodewise SEM train using Machine Learning (ML) | SEMml |
