version 0.11

s3alfisc · Aug 21, 2022 · aba1d76 · aba1d76
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: fwildclusterboot
 Title: Fast Wild Cluster Bootstrap Inference for Linear Models
-Version: 0.10
+Version: 0.11
 Authors@R: 
     c(
     person(given = "Alexander",

diff --git a/NEWS.md b/NEWS.md
@@ -1,3 +1,21 @@
+# fwildclusterboot 0.11
+
++ This release introduces new wild cluster bootstrap variants as described in [MacKinnon, Nielsen & Webb (2022)](https://www.econ.queensu.ca/sites/econ.queensu.ca/files/wpaper/qed_wp_1485.pdf). The implementation is still quite bare-bone: it only allows to test hypotheses of the form $\beta_k = 0$ vs $\beta_k \neq 0$, does not allow for regression weights or fixed effects, and further does not compute confidence intervals. 
+
+You can run one of the 'new' variants - e.g. the "WCR13", by specifying the `boot_algo` function argument accordingly: 
+
+```
+boottest(
+  lm_fit, 
+  param = ~treatment, 
+  clustid = ~group_id1,
+  B = 9999, 
+  impose_null = TRUE,
+  boot_algo = "WCR13"
+)
+```
+
+
 # fwildclusterboot 0.10
 
 + introduces a range of new methods: `nobs()`, `pval()`, `teststat()`, `confint()` and `print()`

diff --git a/README.Rmd b/README.Rmd
@@ -34,6 +34,8 @@ knitr::opts_chunk$set(
 
 The `{fwildclusterboot}` package provides a native R implementation of the fast wild cluster bootstrap algorithm developed in [Roodman et al (2019)](https://econpapers.repec.org/paper/qedwpaper/1406.htm) for regression objects in R.
 
+Additionally, it provides support for multiple wild cluster bootstrap variants as described in [MacKinnon, Nielsen & Webb (2022)](https://www.econ.queensu.ca/sites/econ.queensu.ca/files/wpaper/qed_wp_1485.pdf).
+
 It also ports functionality of [WildBootTests.jl](https://github.com/droodman/WildBootTests.jl) to R via the [JuliaConnectoR](https://github.com/stefan-m-lenz/JuliaConnectoR).
 
 The package's central function is `boottest()`. It allows to test univariate hypotheses using a wild cluster bootstrap at extreme speed: via the 'fast' algorithm, it is possible to run a wild cluster bootstrap with $B = 100.000$ iterations in less than a second!
@@ -139,4 +141,4 @@ If you are in `R`, you can simply run the following command to get the BibTeX ci
 citation("fwildclusterboot")
 ```
 
-Alternatively, if you prefer to cite the "Fast & Wild" paper by Roodman et al, I would be super happy if you mentioned `{fwildclusterboot}` in a footnote! 
+Alternatively, if you prefer to cite the "Fast & Wild" paper by Roodman et al, it would be great if you mentioned `{fwildclusterboot}` in a footnote =) ! 
diff --git a/README.md b/README.md
@@ -27,6 +27,10 @@ the fast wild cluster bootstrap algorithm developed in [Roodman et al
 (2019)](https://econpapers.repec.org/paper/qedwpaper/1406.htm) for
 regression objects in R.
 
+Additionally, it provides support for multiple wild cluster bootstrap
+variants as described in [MacKinnon, Nielsen & Webb
+(2022)](https://www.econ.queensu.ca/sites/econ.queensu.ca/files/wpaper/qed_wp_1485.pdf).
+
 It also ports functionality of
 [WildBootTests.jl](https://github.com/droodman/WildBootTests.jl) to R
 via the
@@ -159,5 +163,5 @@ citation("fwildclusterboot")
 ```
 
 Alternatively, if you prefer to cite the “Fast & Wild” paper by Roodman
-et al, I would be super happy if you mentioned `{fwildclusterboot}` in a
-footnote!
+et al, it would be great if you mentioned `{fwildclusterboot}` in a
+footnote =) !