How do I force specified coefficients in a Linear Regression model to be positive?
Sorry, but on the surface, this sounds like a terrible idea to me: if linear regression gives you negative coefficients for some explanatory variables that you think should be positive, then it means that either your data is "wrong" (typically noisy or too small) or your intuition is misguided. 

I can't see any good reason why one would use a data-driven approach if the goal is to manually force the model in a particular way. This is the equivalent of breaking the thermometer to hide the fever.

I'd suggest the following instead:


In general an unexpected outcome is arguably a good thing, in the sense that it tells us something we didn't know about the data. That's a cue to investigate what happens in the data. Linear regression is simple enough to analyze: one can look at the correlation, plot the relation between the variables etc.
If there's really something suspicious going on with some variables, maybe some errors in the data which make them behave in a way they shouldn't, then it's much better to discard them altogether from the model rather than fixing their coefficient, because this way the model won't rely on them at all.