slightly clean up docs

README.Rmd

@@ -102,7 +102,7 @@ install_github("s3alfisc/fwildclusterboot")
 
 For a longer introduction to `{fwildclusterboot}`, take a look at the [vignette](https://s3alfisc.github.io/fwildclusterboot/articles/fwildclusterboot.html).
 
-```{r}
+```{r, warning = FALSE, message = FALSE}
 library(fwildclusterboot)
 
 # set seed via dqset.seed for engine = "R" & Rademacher, Webb & Normal weights

README.md

@@ -111,20 +111,6 @@ data(voters)
 lm_fit <- lm(proposition_vote ~ treatment  + log_income + as.factor(Q1_immigration) + as.factor(Q2_defense), data = voters)
 # bootstrap inference via boottest()
 lm_boot <- boottest(lm_fit, clustid = c("group_id1"), B = 9999, param = "treatment")
-#> Warning: Please note that the seeding behavior for random number generation for
-#> `boottest()` has changed with `fwildclusterboot` version 0.13.
-#> 
-#> It will no longer be possible to exactly reproduce results produced by versions
-#> lower than 0.13.
-#> 
-#> If your prior results were produced under sufficiently many bootstrap
-#> iterations, none of your conclusions will change.  For more details about this
-#> change, please read the notes in
-#> [news.md](https://cran.r-project.org/web/packages/fwildclusterboot/news/news.html).
-#> This warning is displayed once per session.
-#> Too guarantee reproducibility, don't forget to set a global random seed
-#> **both** via `set.seed()` and `dqrng::dqset.seed()`.
-#> This message is displayed once every 8 hours.
 summary(lm_boot)
 #> boottest.lm(object = lm_fit, param = "treatment", B = 9999, clustid = c("group_id1"))
 #>  
@@ -156,7 +142,7 @@ citation("fwildclusterboot")
 #> A BibTeX entry for LaTeX users is
 #> 
 #>   @Misc{,
-#>     title = {fwildclusterboot: Fast Wild Cluster Bootstrap Inference for Linear Regression Models (Version 0.13.0)},
+#>     title = {fwildclusterboot: Fast Wild Cluster Bootstrap Inference for Linear Regression Models (Version 0.14.0)},
 #>     author = {Alexander Fischer and David Roodman},
 #>     year = {2021},
 #>     url = {https://cran.r-project.org/package=fwildclusterboot},

vignettes/fwildclusterboot.Rmd

@@ -64,7 +64,7 @@ The `boottest()` function has 4 required and several optional arguments. The req
 + param: a character vector of length one - the model parameter to be tested
 + B: the number of bootstrap iterations
 
-```{r}
+```{r, warning = FALSE, message = FALSE}
 # boottest on an object of type lm
 boot_lm <- boottest(
   lm_fit, 
@@ -158,7 +158,7 @@ boot_lm$engine
 
 ## Choice of Bootstrap Weights
 
-Furthermore, the user can choose among four different weighting distribution via the `type` argument: Rademacher, Mammen, Normal and Webb. By default, `boottest()` uses the Rademacher distribution.
+Furthermore, you can choose among four different weighting distribution via the `type` argument: Rademacher, Mammen, Normal and Webb. By default, `boottest()` uses the Rademacher distribution.
 
 
 ```{r}
@@ -186,7 +186,7 @@ if(requireNamespace("modelsummary")){
 
 ## Other function arguments 
 
-Via the function argument `sign_level`, the user can control the significance level of the test. The default value is sign_level = 0.05, which corresponds to a 95\% confindence interval.
+Via the function argument `sign_level`, you can control the significance level of the test. The default value is sign_level = 0.05, which corresponds to a 95\% confindence interval.
 
 ```{r}
 boot_lm_5 <- boottest(
@@ -210,7 +210,7 @@ if(requireNamespace("modelsummary")){
 }
 ```
 
-In the case of multiway clustering, the user might want to specify the bootstrap clustering level. By default, boottest chooses the clustering level with the highest number of clusters as `bootcluster = "max"`. Other choices are the minimum cluster, or independent clustering variables.  
+In the case of multiway clustering, one might want to specify the bootstrap clustering level. By default, boottest chooses the clustering level with the highest number of clusters as `bootcluster = "max"`. Other choices are the minimum cluster, or independent clustering variables.  
 
 ```{r}
 boot_lm1 <- boottest(
@@ -240,7 +240,9 @@ if(requireNamespace("modelsummary")){
 ## Fixed Effects
 
 Last, `boottest()` supports out-projection of fixed effects in the estimation stage via `lfe::felm()` and `fixest::feols()`. 
-Within the bootstrap, the user can choose to project out *only one* fixed effect, which can be set via the `fe` function argument. All other fixed effects specified in either `felm()` or `feols()` are treated as sets of binary regressors.
+Within the bootstrap, it is possible to project out *only one* fixed effect, which can be set via the `fe` function argument. All other fixed effects specified in either `felm()` or `feols()` are treated as sets of binary regressors. Note that
+`boottest.fixest()` currently does not know how to properly handle advanced `fixest`
+syntax for fixed effects, as e.g. varying slopes. 
 
 ```{r}
 
@@ -266,7 +268,7 @@ if(requireNamespace("fixest")){
 
 ## The Subcluster Bootstrap 
 
-In the case of few treated clusters, [MacKinnon and Webb (2018)](https://academic.oup.com/ectj/article-abstract/21/2/114/5078969)  suggest to use subclusters to form the bootstrap distribution. `boottest()` allows the user to specify subclusters via the `bootcluster` argument. 
+In the case of few treated clusters, [MacKinnon and Webb (2018)](https://academic.oup.com/ectj/article-abstract/21/2/114/5078969)  suggest to use subclusters to form the bootstrap distribution. `boottest()` allows to specify subclusters via the `bootcluster` argument. 
 
 ```{r}
 boot_min <- boottest(