first rerender without deletes

Author: EmilHvitfeldt

404.html.md

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 ![](images/evil-come-at-me-bro-cupcake-by-allison-horst.jpg){height="500" fig-alt="recipes rx sticker, modified to add evil eyes and mouth. Flaming topping and to be holding a knife and a cleaver, both of which are escaping the hex frame."}
 
 The page you requested cannot be found (perhaps it was moved or renamed).

about/davis/index.html.md

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 Davis Vaughan is a Software Engineer at RStudio. He is the author or maintainer of several R packages for finance and data analytics, including tidyquant, timetk, tibbletime, sweep, rray and hardhat. He is well-known for this work around creating modeling packages in R. 
 

about/max/index.html.md

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 Dr. Max Kuhn is a Software Engineer at RStudio. He is the author or maintainer of several R packages for predictive modeling including caret, AppliedPredictiveModeling, Cubist, C50 and SparseLDA. He routinely teaches classes in predictive modeling at Predictive Analytics World and UseR! and his publications include work on neuroscience biomarkers, drug discovery, molecular diagnostics and response surface methodology.

books/fes/index.html.md

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 ![](cover.jpg) 
 
 A primary goal of predictive modeling is to find a reliable and effective predictive relationship between an available set of features and an outcome. This book provides an extensive set of techniques for uncovering effective representations of the features for modeling the outcome and for finding an optimal subset of features to improve a model’s predictive performance.

books/moderndive/index.html.md

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 ![](cover.png) 
 
 This book is intended to be a gentle introduction to the practice of analyzing data and answering questions using data the way data scientists, statisticians, data journalists, and other researchers would. Over the course of this book, you will develop your "data science toolbox," equipping yourself with tools such as data visualization, data formatting, data wrangling, data modeling using regression, and statistical inference via hypothesis testing and confidence intervals.

books/smltar/index.html.md

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 ![](cover.png)
 
 This book explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing.

books/tidytext/index.html.md

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 ![](cover.png)
 
 This practical book provides an introduction to text mining using tidy data principles in R, focusing on exploratory data analysis for text. Using tidy data principles can make text mining task easier and more effective; in this book, learn how to manipulate, summarize, and visualize characteristics of text using these methods and R packages from the tidy tool ecosystem.

books/tmwr/index.html.md

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 ![](cover.png)
 
 Modeling of data is integral to science, business, politics, and many other aspects of our lives. The goals of this book are to:

contribute/index.html.md

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 The ecosystem of tidymodels packages would not be possible without the contributions of the R community. No matter your current skills, it's possible to contribute back to tidymodels. Contributions are guided by our design goals.
 
 ## Design goals

find/all/index.html.md

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 Below you'll find searchable tables to help you explore the tidymodels packages and functions. The tables also include links to the relevant reference page to help you navigate the package documentation. Use the following categories to guide you:
 
 * [Search all of tidymodels](all/index.qmd)

find/broom/index.html.md

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-
 ## Asking for help
 
 If you're asking for R help, reporting a bug, or requesting a new feature, you're more likely to succeed if you include a good reproducible example, which is precisely what the [reprex](https://reprex.tidyverse.org/) package is built for. You can learn more about reprex, along with other tips on how to help others help you in the [tidyverse.org help section](https://www.tidyverse.org/help/).

find/index.html.md

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+
 ::::: {#FrontPage}
 
 :::: {.band .first}

find/parsnip/index.html.md

@@ -76,6 +76,11 @@ packages <- c(
 
 pak::pak(packages)
 
+# Manually check for dev versions
+# sub("rstudio/", "", packages) |>
+#   setdiff(c()) |>
+#   sapply(packageVersion)
+
 # Running `library(brulee)` will trigger installation of torch
 library(brulee)
 

find/recipes/index.html.md

@@ -19,5 +19,6 @@ listing:
 
 
 
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 After you know [what you need to get started](/start/) with tidymodels, you can learn more and go further. Find articles here to help you solve specific problems using the tidymodels framework. 
 

help/index.html.md

@@ -284,16 +284,16 @@ gss %>%
 #> # Groups:   replicate [5,000]
 #>    replicate hours
 #>        <int> <dbl>
-#>  1         1  38.6
-#>  2         1  58.6
-#>  3         1  38.6
-#>  4         1  42.6
-#>  5         1  48.6
-#>  6         1  54.6
-#>  7         1  38.6
-#>  8         1  38.6
-#>  9         1  38.6
-#> 10         1  18.6
+#>  1         1 13.6 
+#>  2         1  4.62
+#>  3         1 78.6 
+#>  4         1 33.6 
+#>  5         1 58.6 
+#>  6         1 23.6 
+#>  7         1 38.6 
+#>  8         1 38.6 
+#>  9         1 48.6 
+#> 10         1 78.6 
 #> # ℹ 2,499,990 more rows
 ```
 :::
@@ -318,15 +318,15 @@ gss %>%
 #> # Groups:   replicate [5,000]
 #>    partyid   age replicate
 #>    <fct>   <dbl>     <int>
-#>  1 rep        36         1
+#>  1 ind        36         1
 #>  2 ind        34         1
-#>  3 other      24         1
-#>  4 dem        42         1
-#>  5 dem        31         1
+#>  3 ind        24         1
+#>  4 ind        42         1
+#>  5 rep        31         1
 #>  6 rep        32         1
 #>  7 dem        48         1
 #>  8 ind        36         1
-#>  9 ind        30         1
+#>  9 rep        30         1
 #> 10 rep        33         1
 #> # ℹ 2,499,990 more rows
 ```
@@ -351,16 +351,16 @@ gss %>%
 #> # A tibble: 5,000 × 2
 #>    replicate  stat
 #>        <int> <dbl>
-#>  1         1  39.8
-#>  2         2  40.2
-#>  3         3  40.0
-#>  4         4  40.1
+#>  1         1  39.7
+#>  2         2  41.0
+#>  3         3  40.5
+#>  4         4  40.8
 #>  5         5  39.9
-#>  6         6  39.9
-#>  7         7  38.2
-#>  8         8  39.5
-#>  9         9  40.1
-#> 10        10  39.9
+#>  6         6  39.2
+#>  7         7  39.2
+#>  8         8  40.9
+#>  9         9  41.1
+#> 10        10  39.7
 #> # ℹ 4,990 more rows
 ```
 :::
@@ -383,16 +383,16 @@ gss %>%
 #> # A tibble: 5,000 × 2
 #>    replicate   stat
 #>        <int>  <dbl>
-#>  1         1 -1.31 
-#>  2         2 -0.637
-#>  3         3 -0.240
-#>  4         4  0.429
-#>  5         5 -0.716
-#>  6         6 -0.769
-#>  7         7  0.623
-#>  8         8  0.535
-#>  9         9  1.04 
-#> 10        10 -0.337
+#>  1         1 -0.593
+#>  2         2  0.280
+#>  3         3  1.97 
+#>  4         4 -2.00 
+#>  5         5  2.33 
+#>  6         6  2.21 
+#>  7         7 -0.928
+#>  8         8 -0.329
+#>  9         9 -2.07 
+#> 10        10  0.332
 #> # ℹ 4,990 more rows
 ```
 :::
@@ -474,12 +474,12 @@ p_value
 #> # A tibble: 1 × 1
 #>   p_value
 #>     <dbl>
-#> 1  0.0416
+#> 1  0.0296
 ```
 :::
 
 
-It looks like the p-value is 0.0416, which is pretty small---if the true mean number of hours worked per week was actually 40, the probability of our sample mean being this far (1.382 hours) from 40 would be 0.0416. This may or may not be statistically significantly different, depending on the significance level $\alpha$ you decided on *before* you ran this analysis. If you had set $\alpha = .05$, then this difference would be statistically significant, but if you had set $\alpha = .01$, then it would not be.
+It looks like the p-value is 0.0296, which is pretty small---if the true mean number of hours worked per week was actually 40, the probability of our sample mean being this far (1.382 hours) from 40 would be 0.0296. This may or may not be statistically significantly different, depending on the significance level $\alpha$ you decided on *before* you ran this analysis. If you had set $\alpha = .05$, then this difference would be statistically significant, but if you had set $\alpha = .01$, then it would not be.
 
 To get a confidence interval around our estimate, we can write:
 

index.html.md

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 ## Installation and use
 
 * Install many of the packages in the tidymodels ecosystem by running `install.packages("tidymodels")`.