Comparison between groups I have to compare three groups. Three measurements using 3 different methods on the same subjects.
The variables are not normal, and the measures concern the same people, so dependent samples. I can use Kruskal-Wallis?
I can use Wilcoxon post analysis with Bonferroni correction?
How can I use the Bonferroni correction in R? Can I have examples?
 A: It seems some misunderstanding have arose in comments, so here is an answer.
Why we shouldn't use Wilcoxon test as post-hoc?
Pairwise Wilcox-Mann-whitney test doesn't take into account ranks from Kruskal-Wallis test, it creates it's own. On the other side Dunn's test use group ranking from KW test.
Example with R:
set.seed(pi)
df <- data.frame(val = c(runif(15,  0, 15),
                         runif(15,  0, 15),
                         runif(15, 10, 20)),
                 grp = rep(c('A', 'B', 'C'), each = 15))

kruskal.test(val ~ grp, df)

    Kruskal-Wallis rank sum test

data:  val by grp
Kruskal-Wallis chi-squared = 22.045, df = 2, p-value = 1.633e-05

So Kruskal-Wallis test tells us that there is statistically significant differences between groups. But we need to run Dunn's test to know between which pairs of group we have difference .
require(PMCMR)
posthoc.kruskal.dunn.test(val ~ grp, df)

    Pairwise comparisons using Dunn's-test for multiple 
                         comparisons of independent samples 

data:  val by grp 

  A       B      
B 0.96674 -      
C 0.00013 0.00013

P value adjustment method: holm 

And we can conclude that group C is different from A and B.
Edit
Okey, I missed part about dependent samples.
In case of dependent samples Friedman test with appropriate post-hoc (Conover test in PMCMR for example) should be used. But general approach (just with correct tests) I showed above is still applicable.
