Testing Packages

Stephen Haptonstahl and Jong-Hee Park are developing a test suite for MCMCpack. The purpose of this page is to:

• Document some of the concepts or key code features we are using
• Provide guidance for others trying to test packages

testMCMCpack package

A detailed description of testMCMCpack is in the MCMCpack specification. This package is for the use of developers of MCMCpack and is stored in the MCMCpack subverison repository.

How to Add an MCMC function to testMCMCpack

coming soon

How to Compile this (or any) R Package for Windows

coming soon

testMCMCpack TODO List

• Abstract the plotting of z-score graphs
• Incorporate feedback on v0.1 to complete v0.2
• Neaten the output of the omnibus function testMCMCpack()
  • Suppress "Metropolis" output and all screen printing using R option
  • Display results in a table
• Update this website (of course!)
• Include a nice heading to be shown when people load testMCMCpack
• Look over BayesValidate and link it to testMCMCpack if useful
  • Think about the possibility of generalizing testMCMCpack to other Bayesian R packages

R Features for Testing: not a solution to this problem

R has a very simple way to run a custom test suite automatically when running R CMD check. Unfortunately, our test suite takes a long time to run. Running the beta-version of the test on just MCMClogit took four hours on a dual-processor Xeon. Running all of these tests every time we want to be sure MCMCpack passes R CMD check would not be practical.

However, the features R has to include testing are simple and pretty powerful. Just create a tests folder in the root folder of the package source. Any file with a .R extension, say testFoo.R, in tests will be run automatically. The output is sent to testFoo.Rout in the tests/ folder where the results of R CMD che are saved. Then you can look at the results.

Even better, take a copy of testFoo.Rout that looks good and put it back into the (package root)/tests/ folder with the source (the same folder as testFoo.R. Rename it testFoo.Rout.save. Now, running R CMD check will diff the newly generated testFoo.Rout and the existing testFoo.Rout.save. This means it will compare them line-by-line and display any differences between them.

If you write testFoo.R well, this can be a great tool. We might use it for switch-flipping tests, but testing the posterior distributions takes too long to do this with every R CMD check.