TMB is an R package with functionality similar to ADMB. It requires R at least version 3.0.0 and development tools needed to install R packages from source. The package is installed from the command line by entering the adcomb folder and typing
- make install
To build the manual type
- make pdf
Once the package is successfully installed it can be tested by starting R and running
Tested to work on 64 bit R with latest Rtools. Currently not working with 32 bit R.
Start 64 bit R and change working directory to the (cloned or unzipped)
From R run:
The required Rtools will be downloaded and installed. Note that the PATH variable need not be changed by the installer or otherwise edited. The PATH will be automatically set for each TMB session.
- Parallel user templates work, including changing the number of threads from R.
- Filenames and folders with spaces should be ok.
- -Wall flag disabled by default.
Tested to work with both llvm-gcc-4.2 and clang. Fortran compiler libraries must be installed. According to R admin manual "the OpenMP support in this version of gcc is problematic, and the alternative, clang, has no OpenMP support". So, parallel templates with OS X will require a different compiler installed.
For large 3D random field models the ordering algorithms shipping with R's Matrix package are far from optimal. To get better orderings available run the following in the terminal:
- sudo apt-get install libsuitesparse-metis-3.1.0
This will install a more complete version of CHOLMOD with more orderings available. Then install TMB like this:
- make install-metis
For a quick example of how to use it start R, load TMB and run
On recent versions of gcc the following problem may be encountered: When the user cpp file is changed, re-compiled and re-loaded, the changes does not take place. To see if you are affected by this issue, assuming your compiled DLL is called "mymodel.so", try running:
- readelf -s mymodel.so | grep UNIQUE
If this gives a lot of output it is not possible to unload the library, and R will have to be restarted every time the model is re-compiled. A workaround is to use clang++ instead of gcc.