version bumps for release (#101)

.Rbuildignore

@@ -14,3 +14,4 @@ Applications/Tensorflow.Rmd
 ^CODE_OF_CONDUCT\.md$
 ^LICENSE\.md$
 ^man-roxygen$
+^revdep$

.gitignore

@@ -3,3 +3,5 @@
 .Rproj.user
 .DS_Store
 man/.Rapp.history
+revdep/problems.md
+revdep/README.md

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: embed
 Title: Extra Recipes for Encoding Predictors
-Version: 0.1.4.9000
+Version: 0.1.5
 Authors@R: c(
     person("Max", "Kuhn", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-2402-136X")),

NEWS.md

@@ -1,4 +1,4 @@
-# embed (development version)
+# embed 0.1.5
 
 * Re-licensed package from GPL-2 to MIT. See [consent from copyright holders here](https://github.com/tidymodels/embed/issues/78).
 

README.Rmd

@@ -15,7 +15,7 @@ knitr::opts_chunk$set(
 
 <!-- badges: start -->
 [![R build status](https://github.com/tidymodels/embed/workflows/R-CMD-check/badge.svg)](https://github.com/tidymodels/embed/actions)
-[![Codecov test coverage](https://codecov.io/gh/tidymodels/embed/branch/main/graph/badge.svg)](https://codecov.io/gh/tidymodels/embed?branch=main)
+[![Codecov test coverage](https://codecov.io/gh/tidymodels/embed/branch/main/graph/badge.svg)](https://app.codecov.io/gh/tidymodels/embed?branch=main)
 [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/embed)](https://CRAN.r-project.org/package=embed)
 [![Downloads](https://CRANlogs.r-pkg.org/badges/embed)](https://CRAN.r-project.org/package=embed)
 ![](https://img.shields.io/badge/lifecycle-maturing-blue.svg)
@@ -43,6 +43,8 @@ For numeric predictors:
 
 * `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. 
+
 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

README.md

@@ -6,7 +6,7 @@
 [![R build
 status](https://github.com/tidymodels/embed/workflows/R-CMD-check/badge.svg)](https://github.com/tidymodels/embed/actions)
 [![Codecov test
-coverage](https://codecov.io/gh/tidymodels/embed/branch/main/graph/badge.svg)](https://codecov.io/gh/tidymodels/embed?branch=main)
+coverage](https://codecov.io/gh/tidymodels/embed/branch/main/graph/badge.svg)](https://app.codecov.io/gh/tidymodels/embed?branch=main)
 [![CRAN\_Status\_Badge](https://www.r-pkg.org/badges/version/embed)](https://CRAN.r-project.org/package=embed)
 [![Downloads](https://CRANlogs.r-pkg.org/badges/embed)](https://CRAN.r-project.org/package=embed)
 ![](https://img.shields.io/badge/lifecycle-maturing-blue.svg)
@@ -56,6 +56,9 @@ For numeric predictors:
 -   `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.
+
 Some references for these methods are:
 
 -   Francois C and Allaire JJ (2018) [*Deep Learning with