Is R output reliable (specially IRT package ltm) This may seem like a foolish question, but it is critical for me to have some expert advice on this. 
I am doing some research on students' learning levels in India. I am using R software (ltm package) to calculate student scores (ability estimates as per IRT model). 
Features of the data:
No of students: 15,000
Test papers: 3 different forms
No of questions in one form: 50
My concern is whether R outputs are reliable enough to publish the results in research community or the results from R software can be questioned, specifically with respect to ltm package. Also if R can handle such large data comfortably without compromising the quality.
I would be very grateful if you could share your inputs and any documentation/research paper/reference you may have.
 A: R is widely used in scientific circles for published papers. R stores your data in RAM, so either it will be able to process your data set or it won't -- depending on whether the data and processing fit in memory -- there is no degraded mode where you get results but they are less accurate. (Technically, there are packages that let you work with larger data sets than fit in memory, but it's not trivial to use them.)
There are almost always choices of packages that do similar tasks, so if you are very concerned about ltm, you can also look into other packages that do IRT. A quick search on my machine brings up packages MCMCpack, psych, and KernSmoothIRT, and there are probably others if you look on CRAN. Analyze your data with two packages to make sure that the answers are in reasonable agreement.
The beauty of R is that it's free, so you can try it and see. And the packages are free, so you can try more than one. If your data set is too large, or if you're not satisfied with the results, you've only lost a bit of time.
A: If you, or anyone else, has a question about the results of an analysis in R you/they can always look at the source code to see exactly what computations are being made.  With any proprietary software you have to take their word that it is doing the correct things.  
> library(fortunes)
> fortune(102)

Mingzhai Sun: When you use it [R], since it is written by so many authors, how
do you know that the results are trustable?
Bill Venables: The R engine [...] is pretty well uniformly excellent code but
you have to take my word for that. Actually, you don't. The whole engine is
open source so, if you wish, you can check every line of it. If people were out
to push dodgy software, this is not the way they'd go about it.
   -- Mingzhai Sun and Bill Venables
      R-help (January 2004)

A: I might add that in recent IRT work I am undertaking, when all specifications are made the same, I have found IRT results (2PLM) from the ltm package to map on to the same analyses conducted in the Mplus statistical package extremely well, including model solution indices as well as parameter estimates.
