Package: brulee 0.3.0.9000
Max Kuhn
brulee: High-Level Modeling Functions with 'torch'
Provides high-level modeling functions to define and train models using the 'torch' R package. Models include linear, logistic, and multinomial regression as well as multilayer perceptrons.
Authors:
brulee_0.3.0.9000.tar.gz
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brulee_0.3.0.9000.tgz(r-4.4-any)brulee_0.3.0.9000.tgz(r-4.3-any)
brulee_0.3.0.9000.tar.gz(r-4.5-noble)brulee_0.3.0.9000.tar.gz(r-4.4-noble)
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brulee.pdf |brulee.html✨
brulee/json (API)
NEWS
| # Install 'brulee' in R: |
| install.packages('brulee', repos = c('https://tidymodels.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/tidymodels/brulee/issues
Last updated 1 months agofrom:66e19f643d. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 15 2024 |
| R-4.5-win | OK | Nov 15 2024 |
| R-4.5-linux | OK | Nov 15 2024 |
| R-4.4-win | OK | Nov 15 2024 |
| R-4.4-mac | OK | Nov 15 2024 |
| R-4.3-win | OK | Nov 15 2024 |
| R-4.3-mac | OK | Nov 15 2024 |
Exports:%>%autoplotbrulee_activationsbrulee_linear_regbrulee_logistic_regbrulee_mlpbrulee_mlp_two_layerbrulee_multinomial_regcoefmatrix_to_datasetschedule_cyclicschedule_decay_exposchedule_decay_timeschedule_stepset_learn_ratetunable
Dependencies:bitbit64callrclicolorspacecorodescdplyrellipsisfansifarvergenericsggplot2gluegtablehardhatisobandjsonlitelabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigprocessxpsR6RColorBrewerRcpprlangsafetensorsscalessparsevctrstibbletidyselecttorchutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Activation functions for neural networks in brulee | brulee_activations |
| Fit a linear regression model | brulee_linear_reg brulee_linear_reg.data.frame brulee_linear_reg.default brulee_linear_reg.formula brulee_linear_reg.matrix brulee_linear_reg.recipe |
| Fit a logistic regression model | brulee_logistic_reg brulee_logistic_reg.data.frame brulee_logistic_reg.default brulee_logistic_reg.formula brulee_logistic_reg.matrix brulee_logistic_reg.recipe |
| Fit neural networks | brulee_mlp brulee_mlp.data.frame brulee_mlp.default brulee_mlp.formula brulee_mlp.matrix brulee_mlp.recipe brulee_mlp_two_layer brulee_mlp_two_layer.data.frame brulee_mlp_two_layer.default brulee_mlp_two_layer.formula brulee_mlp_two_layer.matrix brulee_mlp_two_layer.recipe |
| Fit a multinomial regression model | brulee_multinomial_reg brulee_multinomial_reg.data.frame brulee_multinomial_reg.default brulee_multinomial_reg.formula brulee_multinomial_reg.matrix brulee_multinomial_reg.recipe |
| Plot model loss over epochs | autoplot.brulee_linear_reg autoplot.brulee_logistic_reg autoplot.brulee_mlp autoplot.brulee_multinomial_reg brulee-autoplot |
| Extract Model Coefficients | brulee-coefs coef.brulee_linear_reg coef.brulee_logistic_reg coef.brulee_mlp coef.brulee_multinomial_reg |
| Convert data to torch format | matrix_to_dataset |
| Predict from a 'brulee_linear_reg' | predict.brulee_linear_reg |
| Predict from a 'brulee_logistic_reg' | predict.brulee_logistic_reg |
| Predict from a 'brulee_mlp' | predict.brulee_mlp |
| Predict from a 'brulee_multinomial_reg' | predict.brulee_multinomial_reg |
| Change the learning rate over time | schedule_cyclic schedule_decay_expo schedule_decay_time schedule_step set_learn_rate |
