Dear Bugman,

thanks for your reply. My problem with "sm" is two-fold:

**-** first it seems that in "sm" "non-parametric" refers not to the distributional assumption but to the form of the relationship between y and a continuous covariate, i.e. a smoothing issue. This is the discussion I had here:

http://stats.stackexchange.com/questions/55169/how-to-interpret-results-from-non-parametric-ancova
**-** second, even if the first point turns out to be non-limiting for my case (where distributional assumptions are not met), it is uncler to me how to interpret the results from "sm"

E.g. with sm.ancova, for var1 to var4, post as response, pre as co-variate, group as factor, I get:

var1 p-value: non significant for "equality model" and non significant for "parallel model",

var2 p-value: significant for "equality model" and non significant for "parallel model",

var3 p-value: non significant for "equality model" and significant for "parallel model",

var4 p-value: significant for "equality model" and significant for "parallel model".

how to interpret it?

Many thanks!