tidymodels · topepo · Jul 19, 2021 · Jun 25, 2021 · Jun 28, 2021 · Jun 28, 2021
diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
@@ -53,6 +53,21 @@ jobs:
           pak::pkg_install("C50")
         shell: Rscript {0}
 
+      - name: Install Miniconda
+        run: |
+          Rscript -e "pak::pkg_install('rstudio/reticulate')"
+          Rscript -e "reticulate::install_miniconda()"
+
+      - name: Find Miniconda on macOS
+        if: runner.os == 'macOS'
+        run: echo "options(reticulate.conda_binary = reticulate:::miniconda_conda())" >> .Rprofile
+
+      - name: Install TensorFlow
+        run: |
+          reticulate::conda_create('r-reticulate', packages = c('python==3.6.9'))
+          tensorflow::install_tensorflow(version='1.14.0')
+        shell: Rscript {0}
+
       - name: Install package
         run: R CMD INSTALL .
 

diff --git a/_pkgdown.yml b/_pkgdown.yml
@@ -10,7 +10,7 @@ template:
 # https://github.com/tidyverse/tidytemplate for css
 
 development:
-  mode: auto
+  mode: release
 
 
 figures:
@@ -90,6 +90,8 @@ navbar:
        href: https://www.tidymodels.org/learn/develop/models/
      - text: Evaluating submodels with the same model object
        href: articles/articles/Submodels.html
+     - text: Fitting and Predicting with parsnip
+       href: articles/articles/Examples.html
   - text: News
     href: news/index.html
   - text: Reference

diff --git a/man/details_multinom_reg_glmnet.Rd b/man/details_multinom_reg_glmnet.Rd
diff --git a/man/details_nearest_neighbor_kknn.Rd b/man/details_nearest_neighbor_kknn.Rd
diff --git a/man/details_rand_forest_randomForest.Rd b/man/details_rand_forest_randomForest.Rd
diff --git a/man/details_rand_forest_ranger.Rd b/man/details_rand_forest_ranger.Rd
diff --git a/man/details_svm_linear_LiblineaR.Rd b/man/details_svm_linear_LiblineaR.Rd
diff --git a/man/details_svm_linear_kernlab.Rd b/man/details_svm_linear_kernlab.Rd
diff --git a/man/details_svm_poly_kernlab.Rd b/man/details_svm_poly_kernlab.Rd
diff --git a/man/details_svm_rbf_kernlab.Rd b/man/details_svm_rbf_kernlab.Rd
diff --git a/man/rmd/boost_tree_C5.0.Rmd b/man/rmd/boost_tree_C5.0.Rmd
@@ -58,6 +58,10 @@ boost_tree(trees = integer(), min_n = integer(), sample_size = numeric()) %>%
 
 By default, early stopping is used. To use the complete set of boosting iterations, pass `earlyStopping = FALSE` to [set_engine()]. Also, it is unlikely that early stopping will occur if `sample_size = 1`.
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#boost-tree-C5.0) for `boost_tree()` with the `"C5.0"` engine.
+
 ## References
 
 -   Kuhn, M, and K Johnson. 2013. *Applied Predictive Modeling*. Springer.
diff --git a/man/rmd/boost_tree_xgboost.Rmd b/man/rmd/boost_tree_xgboost.Rmd
@@ -93,6 +93,10 @@ If the model specification has `early_stop >= trees`, `early_stop` is converted
 
 parsnip chooses the objective function based on the characteristics of the outcome. To use a different loss, pass the `objective` argument to [set_engine()]. 
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#boost-tree-xgboost) for `boost_tree()` with the `"xgboost"` engine.
+
 ## References
 
  - [XGBoost: A Scalable Tree Boosting System](https://arxiv.org/abs/1603.02754)

diff --git a/man/rmd/decision_tree_C5.0.Rmd b/man/rmd/decision_tree_C5.0.Rmd
@@ -50,6 +50,10 @@ decision_tree(min_n = integer()) %>%
 ```{r child = "template-tree-split-factors.Rmd"}
 ```
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#decision-tree-C5.0) for `decision_tree()` with the `"C5.0"` engine.
+
 ## References
 
 -   Kuhn, M, and K Johnson. 2013. *Applied Predictive Modeling*. Springer.

diff --git a/man/rmd/decision_tree_rpart.Rmd b/man/rmd/decision_tree_rpart.Rmd
@@ -58,6 +58,10 @@ decision_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = dou
 ```{r child = "template-tree-split-factors.Rmd"}
 ```
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#decision-tree-rpart) for `decision_tree()` with the `"rpart"` engine.
+
 ## References
 
 -   Kuhn, M, and K Johnson. 2013. *Applied Predictive Modeling*. Springer.

diff --git a/man/rmd/linear_reg_glmnet.Rmd b/man/rmd/linear_reg_glmnet.Rmd
@@ -54,6 +54,10 @@ linear_reg(penalty = double(1), mixture = double(1)) %>%
 ```
 By default, [glmnet::glmnet()] uses the argument `standardize = TRUE` to center and scale the data. 
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#linear-reg-glmnet) for `linear_reg()` with the `"glmnet"` engine.
+
 ## References
 
  - Hastie, T, R Tibshirani, and M Wainwright. 2015. _Statistical Learning with Sparsity_. CRC Press.

diff --git a/man/rmd/linear_reg_keras.Rmd b/man/rmd/linear_reg_keras.Rmd
@@ -53,6 +53,9 @@ linear_reg(penalty = double(1)) %>%
 ```{r child = "template-same-scale.Rmd"}
 ```
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#linear-reg-keras) for `linear_reg()` with the `"keras"` engine.
 
 ## References
 

diff --git a/man/rmd/linear_reg_lm.Rmd b/man/rmd/linear_reg_lm.Rmd
@@ -20,6 +20,10 @@ linear_reg() %>%
 ```{r child = "template-makes-dummies.Rmd"}
 ```
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#linear-reg-lm) for `linear_reg()` with the `"lm"` engine.
+
 ## References
 
  - Kuhn, M, and K Johnson. 2013. _Applied Predictive Modeling_. Springer.
diff --git a/man/rmd/linear_reg_stan.Rmd b/man/rmd/linear_reg_stan.Rmd
@@ -39,6 +39,10 @@ Note that the `refresh` default prevents logging of the estimation process. Chan
 
 For prediction, the `"stan"` engine can compute posterior  intervals analogous to confidence and prediction intervals. In  these instances, the units are the original outcome and when `std_error = TRUE`, the standard deviation of the posterior  distribution (or posterior predictive distribution as  appropriate) is returned.
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#linear-reg-stan) for `linear_reg()` with the `"stan"` engine.
+
 ## References
 
  - McElreath, R. 2020 _Statistical Rethinking_. CRC Press.
diff --git a/man/rmd/logistic_reg_LiblineaR.Rmd b/man/rmd/logistic_reg_LiblineaR.Rmd
@@ -53,6 +53,10 @@ logistic_reg(penalty = double(1), mixture = double(1)) %>%
 ```{r child = "template-same-scale.Rmd"}
 ```
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#logistic-reg-LiblineaR) for `logistic_reg()` with the `"LiblineaR"` engine.
+
 ## References
 
  - Hastie, T, R Tibshirani, and M Wainwright. 2015. _Statistical Learning with Sparsity_. CRC Press.

diff --git a/man/rmd/logistic_reg_glm.Rmd b/man/rmd/logistic_reg_glm.Rmd
@@ -20,6 +20,10 @@ logistic_reg() %>%
 ```{r child = "template-makes-dummies.Rmd"}
 ```
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#logistic-reg-glm) for `logistic_reg()` with the `"glm"` engine.
+
 ## References
 
  - Kuhn, M, and K Johnson. 2013. _Applied Predictive Modeling_. Springer.
diff --git a/man/rmd/logistic_reg_glmnet.Rmd b/man/rmd/logistic_reg_glmnet.Rmd
@@ -54,6 +54,10 @@ logistic_reg(penalty = double(1), mixture = double(1)) %>%
 ```
 By default, [glmnet::glmnet()] uses the argument `standardize = TRUE` to center and scale the data. 
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#logistic-reg-glmnet) for `logistic_reg()` with the `"glmnet"` engine.
+
 ## References
 
  - Hastie, T, R Tibshirani, and M Wainwright. 2015. _Statistical Learning with Sparsity_. CRC Press.

diff --git a/man/rmd/logistic_reg_keras.Rmd b/man/rmd/logistic_reg_keras.Rmd
@@ -53,6 +53,9 @@ logistic_reg(penalty = double(1)) %>%
 ```{r child = "template-same-scale.Rmd"}
 ```
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#logistic-reg-keras) for `logistic_reg()` with the `"keras"` engine.
 
 ## References
 

diff --git a/man/rmd/logistic_reg_stan.Rmd b/man/rmd/logistic_reg_stan.Rmd
@@ -39,6 +39,10 @@ Note that the `refresh` default prevents logging of the estimation process. Chan
 
 For prediction, the `"stan"` engine can compute posterior intervals analogous to confidence and prediction intervals. In these instances, the units are the original outcome and when `std_error = TRUE`, the standard deviation of the posterior  distribution (or posterior predictive distribution as appropriate) is returned.
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#logistic-reg-stan) for `logistic_reg()` with the `"stan"` engine.
+
 ## References
 
  - McElreath, R. 2020 _Statistical Rethinking_. CRC Press.
diff --git a/man/rmd/mars_earth.Rmd b/man/rmd/mars_earth.Rmd
@@ -61,6 +61,10 @@ An alternate method for using MARs for categorical outcomes can be found in [dis
 ```{r child = "template-makes-dummies.Rmd"}
 ```
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#mars-earth) for `mars()` with the `"earth"` engine.
+
 ## References
 
  - Friedman, J. 1991. "Multivariate Adaptive Regression Splines." _The Annals of Statistics_, vol. 19, no. 1, pp. 1-67.

diff --git a/man/rmd/mlp_keras.Rmd b/man/rmd/mlp_keras.Rmd
@@ -72,6 +72,10 @@ mlp(
 ```{r child = "template-same-scale.Rmd"}
 ```
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#mlp-keras) for `mlp()` with the `"keras"` engine.
+
 ## References
 
  - Kuhn, M, and K Johnson. 2013. _Applied Predictive Modeling_. Springer.

diff --git a/man/rmd/mlp_nnet.Rmd b/man/rmd/mlp_nnet.Rmd
@@ -73,6 +73,10 @@ mlp(
 ```{r child = "template-same-scale.Rmd"}
 ```
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#mlp-nnet) for `mlp()` with the `"nnet"` engine.
+
 ## References
 
  - Kuhn, M, and K Johnson. 2013. _Applied Predictive Modeling_. Springer.

diff --git a/man/rmd/multinom_reg_glmnet.Rmd b/man/rmd/multinom_reg_glmnet.Rmd
@@ -54,6 +54,10 @@ multinom_reg(penalty = double(1), mixture = double(1)) %>%
 ```
 By default, [glmnet::glmnet()] uses the argument `standardize = TRUE` to center and scale the data. 
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#multinom-reg-glmnet) for `multinom_reg()` with the `"glmnet"` engine.
+
 ## References
 
  - Hastie, T, R Tibshirani, and M Wainwright. 2015. _Statistical Learning with Sparsity_. CRC Press.

diff --git a/man/rmd/multinom_reg_keras.Rmd b/man/rmd/multinom_reg_keras.Rmd
@@ -53,6 +53,9 @@ multinom_reg(penalty = double(1)) %>%
 ```{r child = "template-same-scale.Rmd"}
 ```
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#multinom-reg-keras) for `multinom_reg()` with the `"keras"` engine.
 
 ## References
 

diff --git a/man/rmd/multinom_reg_nnet.Rmd b/man/rmd/multinom_reg_nnet.Rmd
@@ -51,6 +51,10 @@ multinom_reg(penalty = double(1)) %>%
 ```{r child = "template-same-scale.Rmd"}
 ```
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#multinom-reg-nnet) for `multinom_reg()` with the `"nnet"` engine.
+
 ## References
 
  - Luraschi, J, K Kuo, and E Ruiz. 2019. _Mastering nnet with R_. O'Reilly Media

diff --git a/man/rmd/nearest_neighbor_kknn.Rmd b/man/rmd/nearest_neighbor_kknn.Rmd
@@ -69,6 +69,10 @@ nearest_neighbor(
 ```{r child = "template-same-scale.Rmd"}
 ```
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#nearest-neighbor-kknn) for `nearest_neighbor()` with the `"kknn"` engine.
+
 ## References
 
  - Hechenbichler K. and Schliep K.P. (2004) [Weighted k-Nearest-Neighbor Techniques and Ordinal Classification](https://epub.ub.uni-muenchen.de/1769/), Discussion Paper 399, SFB 386, Ludwig-Maximilians University Munich

diff --git a/man/rmd/rand_forest_randomForest.Rmd b/man/rmd/rand_forest_randomForest.Rmd
@@ -70,6 +70,10 @@ rand_forest(
 ```{r child = "template-tree-split-factors.Rmd"}
 ```
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#rand-forest-randomForest) for `rand_forest()` with the `"randomForest"` engine.
+
 ## References
 
  - Kuhn, M, and K Johnson. 2013. _Applied Predictive Modeling_. Springer.

diff --git a/man/rmd/rand_forest_ranger.Rmd b/man/rmd/rand_forest_ranger.Rmd
@@ -78,6 +78,10 @@ By default, parallel processing is turned off. When tuning, it is more efficient
 
 For `ranger` confidence intervals, the intervals are  constructed using the form `estimate +/- z * std_error`. For  classification probabilities, these values can fall outside of  `[0, 1]` and will be coerced to be in this range.
 
+## Examples 
+
+The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#rand-forest-ranger) for `rand_forest()` with the `"ranger"` engine.
+
 ## References
 
  - Kuhn, M, and K Johnson. 2013. _Applied Predictive Modeling_. Springer.