[R] Multiple Comparisons-Kruskal-Wallis-Test: kruskal{agricolae} and kruskalmc{pgirmess} don't yield the same results although they should do (?)

[greatest.possible.newbie]

Thank you for your answer.
The p.adj argument in the kruskal()-function doesn't seem to change
anything... Not even the "bonferroni"-method although it is described as the
most conservative one (multiplying all p-values with the number of
comparisons). I suppose the kruskal()-function is not working properly...
On the other hand I doubt the method behind the kruskalmc()-function as this
function doesn't even turn out to detect significant differences between the
grouping variable (which is obviously a severe error).
Do you think it is justifiable to use the kruskal()-function without
p-adjustment, i.e. doing only pairwise tests like you can do with the
kruskal.test()-function although I obviously want to do multiple
comparisons?

kruskal(x[,1],x[,2],p.adj="bonferroni")
#Yields exactely the same results.
#Groups, Treatments and mean of the ranks
#a 	 11 	 304.4 
#ab 	 9  	 296 
#ab 	 7  	 286.6 
#ab 	 8  	 278.2 
#ab 	 10 	 268.7 
#ab 	 2  	 250.6 
#ab 	 6  	 242.9 
#ab 	 1  	 242.1 
#ab 	 3  	 239.4 
#ab 	 5  	 228.8 
#b 	 4  	 219.5 


kruskalmc(x[,2],x[,2])

#Multiple comparison test after Kruskal-Wallis 
#p.value: 0.05 
#Comparisons
#      obs.dif critical.dif difference
#[......]
#6-9      54.0    162.02688      FALSE
#6-10     69.5    159.04584      FALSE
#6-11     94.5    133.02196      FALSE
#7-8      18.0    160.00778      FALSE
#7-9      35.0    169.78370      FALSE
#7-10     50.5    166.94123      FALSE
#7-11     75.5    142.36796      FALSE
#8-9      17.0    165.54197      FALSE
#8-10     32.5    162.62538      FALSE
#8-11     57.5    137.28174      FALSE
#9-10     15.5    172.25281      FALSE
#9-11     40.5    148.56074      FALSE
#10-11    25.0    145.30369      FALSE



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