Update title in README; rebuild

04_ancova/exercises-ancova2.md

@@ -4,9 +4,7 @@ measurements
 
 ## MASS anorexia data
 
-1.  Analyze the original data:
+Data preparation
-    - In R, see ?MASS::anorexia
-    - Data preparation
 
 ``` r
 data(anorexia, package = "MASS")
@@ -20,11 +18,13 @@ lattice::xyplot(Postwt ~ Prewt, dat, groups = Treat,
                 type = c("g", "r", "p"), auto.key = TRUE)
 ```
 
-- Estimate the average treatment effect (ATE) for FT relative to CBT.
+1.  Analyze the original data:
+    - In R, see ?MASS::anorexia
+    - Estimate the average treatment effect (ATE) for FT relative to
+      CBT.
     - What is the 95% CI for the ATE?
     - What are the pre- and post-weight means for the two groups?
     - What are the baseline-adjusted means for the two groups?
-
 2.  Run a power simulation for a replication study:
     - Draw plausible pre-weights.
     - Specify the minimum relevant effect.
@@ -41,5 +41,3 @@ lattice::xyplot(Postwt ~ Prewt, dat, groups = Treat,
     - the R code, output, and plots (if any)
 
     Render the R or Rmd file to HTML.
-
-### Reference

05_mixed1/powersim-lmm.md

@@ -3,8 +3,8 @@ Power for mixed-effects models
 Last modified: 2026-01-09
 
 ``` r
-library(lattice)
+library("lattice")
-library(lme4)
+library("lme4")
 ```
 
 # Reanalysis

06_mixed2/exercises-datsimlmm.md

@@ -8,8 +8,8 @@ Exercises: Data simulation for crossed random-effects models
   “manually”
 
 ``` r
-library(lattice)
+library("lattice")
-library(lme4)
+library("lme4")
 
 #--------------- (1) Create data frame ----------------------------------------
 datsim <- expand.grid(subject = factor(c("s1" , "s2" , "s3" )),

README.md

@@ -1,6 +1,5 @@
-Power simulations
+Methods Workshop WS 2025/26: Power simulations
 ================
-Nora Wickelmaier
 January, 21-22, 2026
 
 ## Schedule