diff --git a/docs/articles/jlmerclusterperm.html b/docs/articles/jlmerclusterperm.html index 8c0b7e3..283f394 100644 --- a/docs/articles/jlmerclusterperm.html +++ b/docs/articles/jlmerclusterperm.html @@ -140,9 +140,7 @@
jlmerclusterperm
provides both a wholesale and a
piecemeal approach to conducting a CPA. The main workhorse function in
the package is clusterpermute()
, which is composed of five
diff --git a/vignettes/jlmerclusterperm.Rmd b/vignettes/jlmerclusterperm.Rmd
index c6e38a4..9ead148 100644
--- a/vignettes/jlmerclusterperm.Rmd
+++ b/vignettes/jlmerclusterperm.Rmd
@@ -30,7 +30,9 @@ The CPA procedure identifies empirical clusters in a time series data and tests
## Package design
-![jlmerclusterperm package function design](https://raw.githubusercontent.com/yjunechoe/jlmerclusterperm/main/man/figures/jlmerclusterperm_fn_design.png)
+```{r, echo = FALSE, out.width="100%"}
+knitr::include_graphics("https://raw.githubusercontent.com/yjunechoe/jlmerclusterperm/main/man/figures/jlmerclusterperm_fn_design.png")
+```
`jlmerclusterperm` provides both a wholesale and a piecemeal approach to conducting a CPA. The main workhorse function in the package is `clusterpermute()`, which is composed of five smaller functions that are called internally in succession. The smaller functions representing the algorithmic steps of a CPA are also exported, to allow more control over the procedure (e.g., for debugging and diagnosing a CPA run).