To the first question, I don't have any information about jMetrick.
In applying IRT, (as with any other statistical procedure) the first step is to use it with as many different kinds of data as possible. There is a learning curve, but I believe that it is worth it.
One important feature of IRT is the differentiation between Rasch models and IRT models. They were developed by different people for different purposes. That being said, IRT models are a superset of Rasch models.
Rasch models are one parameter models - they assume that all items on a questionnaire are equally predictive of the latent trait.
IRT models, however are two parameter models which allow the questions to differ in their ability to provide information about the ability of participants.
In addition, there are three parameter models which are like the IRT models, except that they allow for a guessing parameter to account for participants ability to get the right answer by chance (this is more of a concern in ability rather than personality tests).
In addition, there is multidimensional IRT which estimates multiple latent abilities at once. I don't know much about this, but its an area which I intend to learn more.
There is also a distinction between dichotomous and polytomous IRT methods. Dichotomous IRT models are those used in ability tests, which have a right and wrong answer. Polytomous IRT models are used in personality tests, where there are multiple answers, which are equally right (in the sense that there is no correct answer).
I personally use R for item response theory. There are two main packages that I have used,
eRm which fits Rasch models only, and
ltm which fits item response theory models (two and three parameter models). Both have similiar functionality, and both provide more routines for dichotomous IRT models. I don't know if R is the "best" for IRT, it does not have all of the multitude of IRT models available, but it is certainly the most extensible, in that one can program these models relatively easily.
I use IRT almost exclusively for polytomous models, in R. I typically start with non parametric IRT methods (provided in the package
mokken) to test the assumptions, and then proceed with a rasch model, adding more complexity as required to get good fit.
For multidimensional IRT, there is the package `mirt', which provides this functionality. I have not used it so I cannot really comment.
If you do install these packages into R, and call the 'vignette("packagename")' function then you should get some useful vignettes (definitely for
mokken, possibly for the others) which may prove useful for you (depending on your level of mathematical sophistication).
Finally, there are a number of good books available for rasch and irt models. Item response theory for psychologists is often used (though I didn't like the style), and further up the technical sophistication chain, there are two extremely comprehensive and useful textbooks - the Handbook of Modern Item Response Theory and Rasch Models:Foundations, Recent Developments and Applications.
I hope this helps.