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‎.github/workflows/R-CMD-check.yaml‎

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extra-packages: any::rcmdcheck
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needs: check
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- name: Install reticulate
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run: pak::pkg_install('reticulate')
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- name: Install dev reticulate
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run: pak::pkg_install('rstudio/reticulate')
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shell: Rscript {0}
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- name: Install Miniconda
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# conda can fail at downgrading python, so we specify python version in advance
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env:
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RETICULATE_MINICONDA_PYTHON_VERSION: "3.8"
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run: reticulate::install_miniconda() # creates r-reticulate conda env by default
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shell: Rscript {0}
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- uses: actions/setup-python@v4
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with:
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python-version: 3.11
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- name: Install TensorFlow
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run: |
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tensorflow::install_tensorflow(version='2.13', conda_python_version = NULL)
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reticulate::virtualenv_create('r-reticulate', python='3.11')
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reticulate::use_virtualenv('r-reticulate')
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tensorflow::install_tensorflow(version='2.16')
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shell: Rscript {0}
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- uses: r-lib/actions/check-r-package@v2

‎.github/workflows/pkgdown.yaml‎

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needs: website
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- name: Install Miniconda
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# conda can fail at downgrading python, so we specify python version in advance
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env:
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RETICULATE_MINICONDA_PYTHON_VERSION: "3.8"
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run: reticulate::install_miniconda() # creates r-reticulate conda env by default
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run: |
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reticulate::install_miniconda()
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shell: Rscript {0}
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- name: Install TensorFlow
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run: |
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tensorflow::install_tensorflow(version='2.13', conda_python_version = NULL)
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reticulate::conda_create('r-reticulate', packages = c('python==3.11'))
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tensorflow::install_tensorflow(version='2.16')
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shell: Rscript {0}
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- name: Install Torch

‎.github/workflows/test-coverage.yaml‎

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shell: Rscript {0}
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- name: Install Miniconda
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# conda can fail at downgrading python, so we specify python version in advance
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env:
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RETICULATE_MINICONDA_PYTHON_VERSION: "3.8"
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run: reticulate::install_miniconda() # creates r-reticulate conda env by default
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run: |
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reticulate::install_miniconda()
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shell: Rscript {0}
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- name: Install TensorFlow
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run: |
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tensorflow::install_tensorflow(version='2.13', conda_python_version = NULL)
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reticulate::conda_create('r-reticulate', packages = c('python==3.11'))
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tensorflow::install_tensorflow(version='2.16')
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shell: Rscript {0}
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- name: Test coverage

‎_pkgdown.yml‎

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navbar:
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components:
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home: ~
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home:
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tutorials:
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text: Learn more
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menu:
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primary: "#CA225E"
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includes:
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in_header: |
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<script defer data-domain="parsnip.tidymodels.org,all.tidymodels.org" src="https://plausible.io/js/plausible.js"></script>
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in_header: |
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<script src="https://cdn.jsdelivr.net/gh/posit-dev/supported-by-posit/js/badge.min.js" data-max-height="43" data-hide-below="1200" data-light-bg="#666f76" data-light-fg="#f9f9f9"></script>
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<script defer data-domain="parsnip.tidymodels.org,all.tidymodels.org" src="https://plausible.io/js/plausible.js"></script>
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development:
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mode: auto
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fig.height: 5.75
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reference:
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- title: Models
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contents:
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- auto_ml
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- bag_mars
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- bag_mlp
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- bag_tree
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- bart
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- boost_tree
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- cubist_rules
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- C5_rules
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- decision_tree
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- discrim_flexible
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- discrim_linear
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- discrim_quad
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- discrim_regularized
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- gen_additive_mod
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- glm_grouped
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- linear_reg
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- logistic_reg
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- mars
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- mlp
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- multinom_reg
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- naive_Bayes
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- nearest_neighbor
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- null_model
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- pls
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- poisson_reg
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- proportional_hazards
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- rand_forest
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- rule_fit
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- survival_reg
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- svm_linear
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- svm_poly
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- svm_rbf
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- title: Infrastructure
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contents:
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- autoplot.model_fit
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- add_rowindex
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- augment.model_fit
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- case_weights
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- case_weights_allowed
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- descriptors
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- extract-parsnip
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- fit.model_spec
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- fit_xy
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- control_parsnip
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- glance.model_fit
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- matrix_to_quantile_pred
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- model_fit
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- model_formula
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- model_spec
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- multi_predict
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- parsnip_addin
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- predict.model_fit
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- reexports
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- repair_call
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- set_args
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- set_engine
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- set_mode
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- show_engines
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- sparse_data
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- tidy.model_fit
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- translate
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- starts_with("update")
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- matches("_train")
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- title: Models
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contents:
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- auto_ml
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- bag_mars
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- bag_mlp
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- bag_tree
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- bart
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- boost_tree
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- cubist_rules
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- C5_rules
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- decision_tree
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- discrim_flexible
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- discrim_linear
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- discrim_quad
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- discrim_regularized
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- gen_additive_mod
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- glm_grouped
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- linear_reg
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- logistic_reg
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- mars
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- mlp
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- multinom_reg
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- naive_Bayes
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- nearest_neighbor
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- null_model
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- pls
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- poisson_reg
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- proportional_hazards
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- rand_forest
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- rule_fit
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- survival_reg
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- svm_linear
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- svm_poly
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- svm_rbf
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- title: Infrastructure
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contents:
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- autoplot.model_fit
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- add_rowindex
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- augment.model_fit
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- case_weights
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- case_weights_allowed
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- descriptors
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- extract-parsnip
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- fit.model_spec
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- fit_xy
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- control_parsnip
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- glance.model_fit
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- matrix_to_quantile_pred
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- model_fit
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- model_formula
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- model_spec
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- multi_predict
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- parsnip_addin
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- predict.model_fit
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- reexports
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- repair_call
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- set_args
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- set_engine
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- set_mode
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- show_engines
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- sparse_data
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- tidy.model_fit
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- translate
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- starts_with("update")
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- matches("_train")
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- title: Developer tools
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contents:
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- condense_control
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- contr_one_hot
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- set_new_model
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- maybe_matrix
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- min_cols
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- max_mtry_formula
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- required_pkgs
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- required_pkgs.model_spec
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- req_pkgs
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- .extract_surv_status
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- .extract_surv_time
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- .model_param_name_key
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- .get_prediction_column_names
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- title: Developer tools
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contents:
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- condense_control
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- contr_one_hot
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- set_new_model
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- maybe_matrix
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- min_cols
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- max_mtry_formula
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- required_pkgs
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- required_pkgs.model_spec
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- req_pkgs
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- .extract_surv_status
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- .extract_surv_time
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- .model_param_name_key
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- .get_prediction_column_names

‎vignettes/articles/Examples.Rmd‎

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Now we create the model fit object:
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```{r}
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#| eval: false
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set.seed(1)
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linreg_reg_fit <- linreg_reg_spec |> fit(ridership ~ ., data = Chicago_train)
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linreg_reg_fit
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```{r}
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#| eval: false
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predict(linreg_reg_fit, Chicago_test)
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```
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```{r}
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#| eval: false
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set.seed(1)
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logreg_cls_fit <- logreg_cls_spec |> fit(Class ~ ., data = data_train)
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logreg_cls_fit
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```{r}
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#| eval: false
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bind_cols(
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predict(logreg_cls_fit, data_test),
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predict(logreg_cls_fit, data_test, type = "prob")
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```{r}
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#| eval: false
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set.seed(1)
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mlp_reg_fit <- mlp_reg_spec |> fit(ridership ~ ., data = Chicago_train)
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mlp_reg_fit
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```{r}
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#| eval: false
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predict(mlp_reg_fit, Chicago_test)
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```
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```{r}
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#| eval: false
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mlp_cls_fit <- mlp_cls_spec |> fit(Class ~ ., data = data_train)
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```{r}
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#| eval: false
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bind_cols(
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predict(mlp_cls_fit, data_test),
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predict(mlp_cls_fit, data_test, type = "prob")
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```{r}
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#| eval: false
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set.seed(1)
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mr_cls_fit <- mr_cls_spec |> fit(island ~ ., data = penguins_train)
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mr_cls_fit
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```{r}
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#| eval: false
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bind_cols(
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predict(mr_cls_fit, penguins_test),
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