devtools::build_readme()

README.md

@@ -25,55 +25,57 @@ dependencies, [`rstanarm`](https://CRAN.r-project.org/package=rstanarm),
 
 Some steps handle categorical predictors:
 
-- `step_lencode_glm()`, `step_lencode_bayes()`, and
-  `step_lencode_mixed()` estimate the effect of each of the factor
-  levels on the outcome and these estimates are used as the new
-  encoding. The estimates are estimated by a generalized linear model.
-  This step can be executed without pooling (via `glm`) or with partial
-  pooling (`stan_glm` or `lmer`). Currently implemented for numeric and
-  two-class outcomes.
-
-- `step_embed()` uses `keras::layer_embedding` to translate the original
-  *C* factor levels into a set of *D* new variables (\< *C*). The model
-  fitting routine optimizes which factor levels are mapped to each of
-  the new variables as well as the corresponding regression coefficients
-  (i.e., neural network weights) that will be used as the new encodings.
-
-- `step_woe()` creates new variables based on weight of evidence
-  encodings.
-
-- `step_feature_hash()` can create indicator variables using feature
-  hashing.
+-   `step_lencode_glm()`, `step_lencode_bayes()`, and
+    `step_lencode_mixed()` estimate the effect of each of the factor
+    levels on the outcome and these estimates are used as the new
+    encoding. The estimates are estimated by a generalized linear model.
+    This step can be executed without pooling (via `glm`) or with
+    partial pooling (`stan_glm` or `lmer`). Currently implemented for
+    numeric and two-class outcomes.
+
+-   `step_embed()` uses `keras::layer_embedding` to translate the
+    original *C* factor levels into a set of *D* new variables (\< *C*).
+    The model fitting routine optimizes which factor levels are mapped
+    to each of the new variables as well as the corresponding regression
+    coefficients (i.e., neural network weights) that will be used as the
+    new encodings.
+
+-   `step_woe()` creates new variables based on weight of evidence
+    encodings.
+
+-   `step_feature_hash()` can create indicator variables using feature
+    hashing.
 
 For numeric predictors:
 
-- `step_umap()` uses a nonlinear transformation similar to t-SNE but can
-  be used to project the transformation on new data. Both supervised and
-  unsupervised methods can be used.
+-   `step_umap()` uses a nonlinear transformation similar to t-SNE but
+    can be used to project the transformation on new data. Both
+    supervised and unsupervised methods can be used.
 
-- `step_discretize_xgb()` and `step_discretize_cart()` can make binned
-  versions of numeric predictors using supervised tree-based models.
+-   `step_discretize_xgb()` and `step_discretize_cart()` can make binned
+    versions of numeric predictors using supervised tree-based models.
 
-- `step_pca_sparse()` and `step_pca_sparse_bayes()` conduct feature
-  extraction with sparsity of the component loadings.
+-   `step_pca_sparse()` and `step_pca_sparse_bayes()` conduct feature
+    extraction with sparsity of the component loadings.
 
 Some references for these methods are:
 
-- Francois C and Allaire JJ (2018) [*Deep Learning with
-  R*](https://www.manning.com/books/deep-learning-with-r), Manning
-- Guo, C and Berkhahn F (2016) “[Entity Embeddings of Categorical
-  Variables](https://arxiv.org/abs/1604.06737)”
-- Micci-Barreca D (2001) “[A preprocessing scheme for high-cardinality
-  categorical attributes in classification and prediction
-  problems](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=A+preprocessing+scheme+for+high-cardinality+categorical+attributes+in+classification+and+prediction+problems&btnG=),”
-  ACM SIGKDD Explorations Newsletter, 3(1), 27-32.
-- Zumel N and Mount J (2017) “[`vtreat`: a `data.frame` Processor for
-  Predictive Modeling](https://arxiv.org/abs/1611.09477)”
-- McInnes L and Healy J (2018) [UMAP: Uniform Manifold Approximation and
-  Projection for Dimension Reduction](https://arxiv.org/abs/1802.03426)
-- Good, I. J. (1985), “[Weight of evidence: A brief
-  survey](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Weight+of+evidence%3A+A+brief+survey&btnG=)”,
-  Bayesian Statistics, 2, pp.249-270.
+-   Francois C and Allaire JJ (2018) [*Deep Learning with
+    R*](https://www.manning.com/books/deep-learning-with-r), Manning
+-   Guo, C and Berkhahn F (2016) “[Entity Embeddings of Categorical
+    Variables](https://arxiv.org/abs/1604.06737)”
+-   Micci-Barreca D (2001) “[A preprocessing scheme for high-cardinality
+    categorical attributes in classification and prediction
+    problems](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=A+preprocessing+scheme+for+high-cardinality+categorical+attributes+in+classification+and+prediction+problems&btnG=),”
+    ACM SIGKDD Explorations Newsletter, 3(1), 27-32.
+-   Zumel N and Mount J (2017) “[`vtreat`: a `data.frame` Processor for
+    Predictive Modeling](https://arxiv.org/abs/1611.09477)”
+-   McInnes L and Healy J (2018) [UMAP: Uniform Manifold Approximation
+    and Projection for Dimension
+    Reduction](https://arxiv.org/abs/1802.03426)
+-   Good, I. J. (1985), “[Weight of evidence: A brief
+    survey](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Weight+of+evidence%3A+A+brief+survey&btnG=)”,
+    Bayesian Statistics, 2, pp.249-270.
 
 ## Getting Started
 
@@ -113,18 +115,18 @@ This project is released with a [Contributor Code of
 Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html).
 By contributing to this project, you agree to abide by its terms.
 
-- For questions and discussions about tidymodels packages, modeling, and
-  machine learning, please [post on RStudio
-  Community](https://community.rstudio.com/new-topic?category_id=15&tags=tidymodels,question).
+-   For questions and discussions about tidymodels packages, modeling,
+    and machine learning, please [post on RStudio
+    Community](https://community.rstudio.com/new-topic?category_id=15&tags=tidymodels,question).
 
-- If you think you have encountered a bug, please [submit an
-  issue](https://github.com/tidymodels/embed/issues).
+-   If you think you have encountered a bug, please [submit an
+    issue](https://github.com/tidymodels/embed/issues).
 
-- Either way, learn how to create and share a
-  [reprex](https://reprex.tidyverse.org/articles/articles/learn-reprex.html)
-  (a minimal, reproducible example), to clearly communicate about your
-  code.
+-   Either way, learn how to create and share a
+    [reprex](https://reprex.tidyverse.org/articles/articles/learn-reprex.html)
+    (a minimal, reproducible example), to clearly communicate about your
+    code.
 
-- Check out further details on [contributing guidelines for tidymodels
-  packages](https://www.tidymodels.org/contribute/) and [how to get
-  help](https://www.tidymodels.org/help/).
+-   Check out further details on [contributing guidelines for tidymodels
+    packages](https://www.tidymodels.org/contribute/) and [how to get
+    help](https://www.tidymodels.org/help/).