version bump amd updates

DESCRIPTION

@@ -1,13 +1,13 @@
 Package: embed
-Version: 0.1.2.9000
+Version: 0.1.3
 Title: Extra Recipes for Encoding Categorical Predictors
 Description: Predictors can be converted to one or more numeric representations using simple generalized linear models <arXiv:1611.09477> or nonlinear models <arXiv:1604.06737>. Most encoding methods are supervised.
 Authors@R: c(
     person(given = "Max", family = "Kuhn", email = "[email protected]", role = c("aut", "cre")),
     person("RStudio", role = "cph"))
 Depends: 
     R (>= 3.1),
-    recipes (>= 0.1.14.9000)
+    recipes (>= 0.1.15)
 Imports: 
     rstanarm,
     keras,
@@ -41,6 +41,3 @@ Roxygen: list(markdown = TRUE)
 ByteCompile: true
 URL: https://embed.tidymodels.org, https://github.com/tidymodels/embed
 BugReports: https://github.com/tidymodels/embed/issues
-Remotes:
-    tidymodels/recipes
-

NEWS.md

@@ -1,4 +1,4 @@
-# embed (development version)
+# embed 0.1.3
 
  * More changes to enable better parallel processing on windows. 
 

README.Rmd

@@ -16,16 +16,16 @@ 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/master/graph/badge.svg)](https://codecov.io/gh/tidymodels/embed?branch=master)
-[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/embed)](http://cran.r-project.org/web/packages/embed)
-[![Downloads](http://cranlogs.r-pkg.org/badges/embed)](http://cran.rstudio.com/package=embed)
+[![CRAN_Status_Badge](http://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)
 <!-- badges: end -->
 
 ## Introduction
 
 `embed` is a package that contains extra steps for the [`recipes`](https://recipes.tidymodels.org/) package for embedding predictors into one or more numeric columns. All of the preprocessing methods are _supervised_. 
 
-These steps are contained in a separate package because the package dependencies, [`rstanarm`](http://cran.rstudio.com/package=rstanarm), [`lme4`](http://cran.rstudio.com/package=lme4), and [`keras`](http://cran.rstudio.com/package=keras),  are fairly heavy. 
+These steps are contained in a separate package because the package dependencies, [`rstanarm`](https://CRAN.r-project.org/package=rstanarm), [`lme4`](https://CRAN.r-project.org/package=lme4), and [`keras`](https://CRAN.r-project.org/package=keras),  are fairly heavy. 
 
 The steps for categorical predictors are:
 

README.md

@@ -7,8 +7,8 @@
 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/master/graph/badge.svg)](https://codecov.io/gh/tidymodels/embed?branch=master)
-[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/embed)](http://cran.r-project.org/web/packages/embed)
-[![Downloads](http://cranlogs.r-pkg.org/badges/embed)](http://cran.rstudio.com/package=embed)
+[![CRAN\_Status\_Badge](http://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)
 <!-- badges: end -->
 
@@ -20,9 +20,9 @@ predictors into one or more numeric columns. All of the preprocessing
 methods are *supervised*.
 
 These steps are contained in a separate package because the package
-dependencies, [`rstanarm`](http://cran.rstudio.com/package=rstanarm),
-[`lme4`](http://cran.rstudio.com/package=lme4), and
-[`keras`](http://cran.rstudio.com/package=keras), are fairly heavy.
+dependencies, [`rstanarm`](https://CRAN.r-project.org/package=rstanarm),
+[`lme4`](https://CRAN.r-project.org/package=lme4), and
+[`keras`](https://CRAN.r-project.org/package=keras), are fairly heavy.
 
 The steps for categorical predictors are: