Disclaimer - I do not know anything about this package. I'll say some generalities which might give you a hint. I haven't looked at implementations of discrete Weibull-regression but for continuous Weibull every implementation uses its own paremetrization/ definition of the parameters. 

I prefer the parametrization:
$$F_W(x)=1-e^{-(\frac{x}{\alpha})^\beta}$$ and so in the discrete case $$Pr( W\leq x+1) =Pr(W_d \leq x) = F(x+1)$$ which is the one used in the [wiki-article][1] (disclaimer, I wrote it). 

Typically output from these types of packages outputs parameters for location-scale parametrization-transformed variables s.t we estimate the parameters of a Gumbal-distribution:
$$F_G(y)=1-\exp(-\exp(\frac{y-\mu}{\sigma}))=F_G(\log(x))=F_W(x)$$ with $\mu = \log(\alpha)$, $\sigma = \frac{1}{\beta}$. The estimation is then done s.t $\log(\alpha)$ is a linear combination of data.

Depending on the true value of $\alpha$ i.e the resolution of your discrete distribution the estimated $\alpha$ and $\beta$ **might not be unique** and in particular, your estimators **might not be consistent** i.e your expected to get the right result. (I've yet found proof telling me anything else). From this it follows that I'd be very careful in interpreting it too much.

I'll give you some hints from the continuous Weibull:

 * Look at the Gumbal distribution and think about the normal distribution.. draw analogies from that..
 * About 63% of events will happen before $x=\alpha$ since $F_W(\alpha)=1-e^{-1}\approx0.632120559$
 * Assume unit $\alpha$. If $\beta< 1$ we have a decreasing hazard: $\beta$ tells you how fast it's decreasing. If $\beta>1$ it tells you how fast it's increasing. 

Check:
https://www.stat.washington.edu/people/fritz/DATAFILES498B2008/ISSTECH-96-022.pdf
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.151.2376&rep=rep1&type=pdf

for the best references on Weibull


  [1]: https://en.wikipedia.org/wiki/Discrete_Weibull_distribution