update docs

s3alfisc · Mar 2, 2023 · d3adb73 · d3adb73
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diff --git a/README.Rmd b/README.Rmd
@@ -18,8 +18,9 @@ knitr::opts_chunk$set(
 
 <!-- badges: start -->
 [![R-CMD-check](https://github.com/s3alfisc/wildrwolf/workflows/R-CMD-check/badge.svg)](https://github.com/s3alfisc/wildrwolf/actions)
-[![pkgcheck](https://github.com/s3alfisc/wildrwolf/workflows/pkgcheck/badge.svg)](https://github.com/s3alfisc/wildrwolf/actions?query=workflow%3Apkgcheck)
+[![](http://cranlogs.r-pkg.org/badges/last-month/wildrwolf)](https://cran.r-project.org/package=wildrwolf)
 [![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html)
+[![](https://www.r-pkg.org/badges/version/wildrwolf)](https://cran.r-project.org/package=wildrwolf)
 ![runiverse-package](https://s3alfisc.r-universe.dev/badges/wildrwolf)
 [![Codecov test coverage](https://codecov.io/gh/s3alfisc/wildrwolf/branch/main/graph/badge.svg)](https://app.codecov.io/gh/s3alfisc/wildrwolf?branch=main)
 <!-- badges: end -->
@@ -34,9 +35,11 @@ Adding support for multi-way clustering is work in progress.
 
 ## Installation
 
-You can install the development version from [GitHub](https://github.com/) with:
+You can install the package from CRAN and the development version from [GitHub](https://github.com/) with:
 
 ``` r
+install.packages("wildrwolf")
+
 # install.packages("devtools")
 devtools::install_github("s3alfisc/wildrwolf")
 

diff --git a/README.md b/README.md
@@ -6,9 +6,10 @@
 <!-- badges: start -->
 
 [![R-CMD-check](https://github.com/s3alfisc/wildrwolf/workflows/R-CMD-check/badge.svg)](https://github.com/s3alfisc/wildrwolf/actions)
-[![pkgcheck](https://github.com/s3alfisc/wildrwolf/workflows/pkgcheck/badge.svg)](https://github.com/s3alfisc/wildrwolf/actions?query=workflow%3Apkgcheck)
+[![](http://cranlogs.r-pkg.org/badges/last-month/wildrwolf)](https://cran.r-project.org/package=wildrwolf)
 [![Lifecycle:
 experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html)
+[![](https://www.r-pkg.org/badges/version/wildrwolf)](https://cran.r-project.org/package=wildrwolf)
 ![runiverse-package](https://s3alfisc.r-universe.dev/badges/wildrwolf)
 [![Codecov test
 coverage](https://codecov.io/gh/s3alfisc/wildrwolf/branch/main/graph/badge.svg)](https://app.codecov.io/gh/s3alfisc/wildrwolf?branch=main)
@@ -31,10 +32,12 @@ Adding support for multi-way clustering is work in progress.
 
 ## Installation
 
-You can install the development version from
+You can install the package from CRAN and the development version from
 [GitHub](https://github.com/) with:
 
 ``` r
+install.packages("wildrwolf")
+
 # install.packages("devtools")
 devtools::install_github("s3alfisc/wildrwolf")
 
@@ -149,7 +152,7 @@ if(requireNamespace("microbenchmark")){
 #>   |                                                                              |                                                                      |   0%  |                                                                              |=========                                                             |  12%  |                                                                              |==================                                                    |  25%  |                                                                              |==========================                                            |  38%  |                                                                              |===================================                                   |  50%  |                                                                              |============================================                          |  62%  |                                                                              |====================================================                  |  75%  |                                                                              |=============================================================         |  88%  |                                                                              |======================================================================| 100%
 #> Unit: seconds
 #>         expr      min       lq     mean   median       uq      max neval
-#>  Romano-Wolf 5.041018 5.041018 5.041018 5.041018 5.041018 5.041018     1
+#>  Romano-Wolf 3.604916 3.604916 3.604916 3.604916 3.604916 3.604916     1
 ```
 
 ## But does it work? Monte Carlo Experiments

diff --git a/vignettes/articles/Non-Standard-Families-of-Tests.Rmd b/vignettes/articles/Non-Standard-Families-of-Tests.Rmd
@@ -23,14 +23,23 @@ and
 $$
    Y = \beta_0 + \beta_3 X_3 + u
 $$
-and wanted to correct the family of hypotheses 
+and wanted to correct the following family of hypotheses for multiple testing: 
 
-$$H_0: \beta_1 = 0 \text{ vs } H_1: \beta_1 \neq 0$$,
-$$H_0: \beta_2 = 0 \text{ vs } H_1: \beta_2 \neq 0$$,
-$$H_0: \beta_1 + \beta_2 = 0 \text{ vs } H_1: \beta_1 + \beta_2\neq 0$$ and 
-$$H_0: \beta_3 = 0 \text{ vs } H_1: \beta_3 \neq 0.$$
+$$
+H_{0,A}: \beta_1 = 0 \text{ vs } H_{1,A}: \beta_1 \neq 0
+$$
+$$
+H_{0,B}: \beta_2 = 0 \text{ vs } H_{1,B}: \beta_2 \neq 0
+$$
+
+$$
+H_{0,C}: \beta_1 + \beta_2 = 0 \text{ vs } H_{1,C}: \beta_1 + \beta_2\neq 0
+$$ 
+$$
+H_{0,D}: \beta_3 = 0 \text{ vs } H_{1,D}: \beta_3 \neq 0.
+$$
 
-The current API of `rwolf()` does not support such a family of tests. To apply the Romano Wolf corrections for the family of tests, you would have to follow the three steps below. But first, let's simulate some data. 
+Unfortunately, the current API of `rwolf()` does not yet support such a family of tests. To apply the Romano Wolf corrections for this family, you would have to follow three steps outlined below. But first, let's simulate some data. 
 
 ```{r}
 N <- 1000
@@ -42,7 +51,7 @@ cluster <- sample(1:20, N, TRUE)
 df <- data.frame(Y = Y, X1 = X1, X2 = X2, X3 = X3, cluster)
 ```
 
-+ Step 1: Estimate two regression models via `fixest`
+### Step 1: Estimate two regression models via `fixest`
 
 ```{r}
 library(fixest)
@@ -51,7 +60,7 @@ fit1 <- feols(Y ~ X1 + X2, data = df, cluster = ~cluster)
 fit2 <- feols(Y ~ X3, data = df, cluster = ~cluster)
 ```
 
-+ Step 2: Create bootstrapped t-statistics via `fwildclusterboot::boottest()`. Make sure to reset the random seeds, so that all calls to `boottest()` use the same bootstrap weights
+### Step 2: Create bootstrapped t-statistics via `fwildclusterboot::boottest()`. Make sure to reset the random seeds, so that all calls to `boottest()` use the same bootstrap weights
 
 ```{r}
 library(fwildclusterboot)
@@ -77,12 +86,12 @@ t_stat <- Reduce("cbind",t_stat)
 
 ```
 
-+ Step 3: feed the bootstrapped and non-bootstrapped t-statistics into the `get_rwolf_pval()` function
+### Step 3: feed the bootstrapped and non-bootstrapped t-statistics into the `get_rwolf_pval()` function
 
 ```{r}
 get_rwolf_pval(t_stats = t_stat, boot_t_stats = t_boot)
 ```
 
-This returns a vector of pvalues for all hypotheses. 
+This returns a vector of multiple-testing adjusted pvalues for all hypotheses.