I don't consider this an R specific question. The real question is: can you trust other people's code? Or, taking the other perspective: do you think you can do better? (in the time you are willing/able to spend)
Whether the software is open or closed source, does not really matter. The trustworthiness of open source compared to commercial software is a topic of much debate. Arguments exist in favor of either side of the discussion. Nobody can refute that both can (and do) contain bugs of various nature, some more insidious than others.
TL;DR: in my opinion: yes, it's fine to use existing packages if they cover your needs.
My motto, in general, is to try to implement stuff myself if a variety of the following are true:
- I know exactly what I want and how to do it (this is far from trivial for many practical models).
- It is worth the time required to implement and test my own code compared to what already exists. In other words: will I use it often or just once?
- What already exists does not offer all the functionality I need.
- I have a good reason not to trust existing implementations (such as unexpected behaviour when using it).
Personally, I like, use and develop a lot of open source as I believe that to be important, especially in an academic context. In practice, many people only consider using a given algorithm if an implementation is available. Nobody benefits from a large set of implementations of the same thing. It is far better to dispose of one efficient, thoroughly tested and verified implementation of a given method.
People like to believe that if I do it myself, I know it's done properly. Practice contradicts this on regular basis.
Not every package of R has been created in an academic environment
I don't know why you feel that packages created by academics are superior. Sure, the method itself may be more complex/novel, but all bets are off regarding implementation. Researchers are not necessarily the best programmers (in fact, given that that is usually not their specialty I would say the opposite is more likely).
In practice, researchers regularly hack solutions together, sometimes without thorough testing. Naturally, this can lead to all kinds of bad results (most notably silent fails). Personally, I believe one of the reasons for this behaviour is the fact that software output is undervalued in research settings. Some researchers simply rush towards the next publication. Publication of results matter, the software used to get them doesn't really. This leads to software that has not been tested properly when people reinvent the wheel for a single use.
How can we use open source without risking failures in the outcome?
You can't. This also applies to commercial software and software you write yourself with lots of care and love. If you happen to find a way to ensure software has no bugs or caveats, you should instantly make an appointment with Bill Gates (or better yet, tell me about it).
The only solution is to critically evaluate results every step of the way. Never trust software unconditionally, no matter what software it is.
Are there certain quality indicators for packages in general and for R?
Usually, packages that get made publicly available, for example on CRAN, have been subjected to rigorous tests, especially the ones that get used often. People will think twice before making untested garbage publicly available when their credibility is at stake. The number of citations can be considered a quality indicator too in addition to an active developing community.