Release Candidate 1.1.4

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: embed
 Title: Extra Recipes for Encoding Predictors
-Version: 1.1.3.9000
+Version: 1.1.4.9000
 Authors@R: c(
     person("Emil", "Hvitfeldt", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-0679-1945")),

NEWS.md

@@ -1,5 +1,9 @@
 # embed (development version)
 
+# embed 1.1.4
+
+## Improvements
+
 * `step_umap()` has gained `initial` and `target_weight` arguments. (#213)
 
 * Calling `?tidy.step_*()` now sends you to the documentation for `step_*()` where the outcome is documented. (#216)

R/lencode_bayes.R

@@ -83,13 +83,13 @@
 #' Modeling," arXiv:1611.09477
 #'
 #' "Hierarchical Partial Pooling for Repeated Binary Trials"
-#' \url{https://tinyurl.com/stan-pooling}
+#' \url{https://CRAN.R-project.org/package=rstanarm/vignettes/pooling.html}
 #'
 #' "Prior Distributions for `rstanarm` Models"
-#' \url{https://tinyurl.com/stan-priors}
+#' \url{http://mc-stan.org/rstanarm/reference/priors.html}
 #'
 #' "Estimating Generalized (Non-)Linear Models with Group-Specific Terms with
-#' `rstanarm`" \url{https://tinyurl.com/stan-glm-grouped}
+#' `rstanarm`" \url{http://mc-stan.org/rstanarm/articles/glmer.html}
 #'
 #' @examplesIf rlang::is_installed("modeldata")
 #' library(recipes)

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/).

revdep/README.md

@@ -1,101 +1,101 @@
 # Platform
 
-|field    |value                                                        |
-|:--------|:------------------------------------------------------------|
-|version  |R version 4.3.1 (2023-06-16)                                 |
-|os       |macOS Ventura 13.6                                           |
-|system   |aarch64, darwin20                                            |
-|ui       |X11                                                          |
-|language |(EN)                                                         |
-|collate  |en_US.UTF-8                                                  |
-|ctype    |en_US.UTF-8                                                  |
-|tz       |America/Los_Angeles                                          |
-|date     |2023-10-17                                                   |
-|pandoc   |3.1.3 @ /Users/emilhvitfeldt/miniforge3/bin/ (via rmarkdown) |
+|field    |value                                         |
+|:--------|:---------------------------------------------|
+|version  |R version 4.3.2 (2023-10-31)                  |
+|os       |macOS Sonoma 14.3.1                           |
+|system   |aarch64, darwin20                             |
+|ui       |X11                                           |
+|language |(EN)                                          |
+|collate  |en_US.UTF-8                                   |
+|ctype    |en_US.UTF-8                                   |
+|tz       |America/Los_Angeles                           |
+|date     |2024-03-19                                    |
+|pandoc   |2.17.1.1 @ /opt/homebrew/bin/ (via rmarkdown) |
 
 # Dependencies
 
 |package      |old        |new        |Δ  |
 |:------------|:----------|:----------|:--|
-|embed        |1.1.2      |1.1.2.9000 |*  |
-|backports    |1.4.1      |1.4.1      |   |
-|base64enc    |0.1-3      |0.1-3      |   |
-|BH           |1.81.0-1   |1.81.0-1   |   |
-|cli          |3.6.1      |3.6.1      |   |
+|embed        |1.1.3      |1.1.3.9000 |*  |
+|backports    |1.4.1      |NA         |*  |
+|base64enc    |0.1-3      |NA         |*  |
+|BH           |1.84.0-0   |1.84.0-0   |   |
+|cli          |3.6.2      |3.6.2      |   |
 |clock        |0.7.0      |0.7.0      |   |
-|config       |0.3.2      |0.3.2      |   |
-|cpp11        |0.4.6      |0.4.6      |   |
-|data.table   |1.14.8     |1.14.8     |   |
+|config       |0.3.2      |NA         |*  |
+|cpp11        |0.4.7      |0.4.7      |   |
+|data.table   |1.15.2     |1.15.2     |   |
 |diagram      |1.6.5      |1.6.5      |   |
-|digest       |0.6.33     |0.6.33     |   |
-|dplyr        |1.1.3      |1.1.3      |   |
-|dqrng        |0.3.1      |0.3.1      |   |
+|digest       |0.6.35     |0.6.35     |   |
+|dplyr        |1.1.4      |1.1.4      |   |
+|dqrng        |0.3.2      |0.3.2      |   |
 |ellipsis     |0.3.2      |0.3.2      |   |
-|fansi        |1.0.5      |1.0.5      |   |
-|FNN          |1.1.3.2    |1.1.3.2    |   |
+|fansi        |1.0.6      |1.0.6      |   |
+|FNN          |1.1.4      |1.1.4      |   |
 |furrr        |0.3.1      |0.3.1      |   |
-|future       |1.33.0     |1.33.0     |   |
-|future.apply |1.11.0     |1.11.0     |   |
+|future       |1.33.1     |1.33.1     |   |
+|future.apply |1.11.1     |1.11.1     |   |
 |generics     |0.1.3      |0.1.3      |   |
-|globals      |0.16.2     |0.16.2     |   |
-|glue         |1.6.2      |1.6.2      |   |
+|globals      |0.16.3     |0.16.3     |   |
+|glue         |1.7.0      |1.7.0      |   |
 |gower        |1.0.1      |1.0.1      |   |
-|hardhat      |1.3.0      |1.3.0      |   |
-|here         |1.0.1      |1.0.1      |   |
+|hardhat      |1.3.1      |1.3.1      |   |
+|here         |1.0.1      |NA         |*  |
 |ipred        |0.9-14     |0.9-14     |   |
 |irlba        |2.3.5.1    |2.3.5.1    |   |
-|jsonlite     |1.8.7      |1.8.7      |   |
-|keras        |2.13.0     |2.13.0     |   |
-|lava         |1.7.2.1    |1.7.2.1    |   |
-|lifecycle    |1.0.3      |1.0.3      |   |
-|listenv      |0.9.0      |0.9.0      |   |
+|jsonlite     |1.8.8      |NA         |*  |
+|keras        |2.13.0     |NA         |*  |
+|lava         |1.8.0      |1.8.0      |   |
+|lifecycle    |1.0.4      |1.0.4      |   |
+|listenv      |0.9.1      |0.9.1      |   |
 |lubridate    |1.9.3      |1.9.3      |   |
 |magrittr     |2.0.3      |2.0.3      |   |
 |numDeriv     |2016.8-1.1 |2016.8-1.1 |   |
-|parallelly   |1.36.0     |1.36.0     |   |
+|parallelly   |1.37.1     |1.37.1     |   |
 |pillar       |1.9.0      |1.9.0      |   |
 |pkgconfig    |2.0.3      |2.0.3      |   |
-|png          |0.1-8      |0.1-8      |   |
-|processx     |3.8.2      |3.8.2      |   |
+|png          |0.1-8      |NA         |*  |
+|processx     |3.8.4      |NA         |*  |
 |prodlim      |2023.08.28 |2023.08.28 |   |
 |progressr    |0.14.0     |0.14.0     |   |
-|ps           |1.7.5      |1.7.5      |   |
+|ps           |1.7.6      |NA         |*  |
 |purrr        |1.0.2      |1.0.2      |   |
 |R6           |2.5.1      |2.5.1      |   |
-|rappdirs     |0.3.3      |0.3.3      |   |
-|Rcpp         |1.0.11     |1.0.11     |   |
-|RcppAnnoy    |0.0.21     |0.0.21     |   |
+|rappdirs     |0.3.3      |NA         |*  |
+|Rcpp         |1.0.12     |1.0.12     |   |
+|RcppAnnoy    |0.0.22     |0.0.22     |   |
 |RcppProgress |0.4.2      |0.4.2      |   |
-|RcppTOML     |0.2.2      |0.2.2      |   |
-|recipes      |1.0.8      |1.0.8      |   |
-|reticulate   |1.34.0     |1.34.0     |   |
-|rlang        |1.1.1      |1.1.1      |   |
-|rprojroot    |2.0.3      |2.0.3      |   |
+|RcppTOML     |0.2.2      |NA         |*  |
+|recipes      |1.0.10     |1.0.10     |   |
+|reticulate   |1.35.0     |NA         |*  |
+|rlang        |1.1.3      |1.1.3      |   |
+|rprojroot    |2.0.4      |NA         |*  |
 |rsample      |1.2.0      |1.2.0      |   |
-|rstudioapi   |0.15.0     |0.15.0     |   |
-|shape        |1.4.6      |1.4.6      |   |
+|rstudioapi   |0.15.0     |NA         |*  |
+|shape        |1.4.6.1    |1.4.6.1    |   |
 |sitmo        |2.0.2      |2.0.2      |   |
 |slider       |0.3.1      |0.3.1      |   |
 |SQUAREM      |2021.1     |2021.1     |   |
-|stringi      |1.7.12     |1.7.12     |   |
-|stringr      |1.5.0      |1.5.0      |   |
-|tensorflow   |2.14.0     |2.14.0     |   |
-|tfautograph  |0.3.2      |0.3.2      |   |
-|tfruns       |1.5.1      |1.5.1      |   |
+|stringi      |1.8.3      |1.8.3      |   |
+|stringr      |1.5.1      |1.5.1      |   |
+|tensorflow   |2.15.0     |NA         |*  |
+|tfautograph  |0.3.2      |NA         |*  |
+|tfruns       |1.5.2      |NA         |*  |
 |tibble       |3.2.1      |3.2.1      |   |
-|tidyr        |1.3.0      |1.3.0      |   |
-|tidyselect   |1.2.0      |1.2.0      |   |
-|timechange   |0.2.0      |0.2.0      |   |
-|timeDate     |4022.108   |4022.108   |   |
+|tidyr        |1.3.1      |1.3.1      |   |
+|tidyselect   |1.2.1      |1.2.1      |   |
+|timechange   |0.3.0      |0.3.0      |   |
+|timeDate     |4032.109   |4032.109   |   |
 |tzdb         |0.4.0      |0.4.0      |   |
-|utf8         |1.2.3      |1.2.3      |   |
+|utf8         |1.2.4      |1.2.4      |   |
 |uwot         |0.1.16     |0.1.16     |   |
-|vctrs        |0.6.4      |0.6.4      |   |
-|warp         |0.2.0      |0.2.0      |   |
-|whisker      |0.4.1      |0.4.1      |   |
-|withr        |2.5.1      |2.5.1      |   |
-|yaml         |2.3.7      |2.3.7      |   |
-|zeallot      |0.1.0      |0.1.0      |   |
+|vctrs        |0.6.5      |0.6.5      |   |
+|warp         |0.2.1      |0.2.1      |   |
+|whisker      |0.4.1      |NA         |*  |
+|withr        |3.0.0      |3.0.0      |   |
+|yaml         |2.3.8      |NA         |*  |
+|zeallot      |0.1.0      |NA         |*  |
 
 # Revdeps