Installation

For using the VineCopulaMatlab toolbox you should first check whether all dependencies are installed.

Dependencies

A C++-compiler being compatible with the used MATLAB release (cf. http://www.mathworks.de/support/compilers for informations about compatible compilers for different releases and opperating systems).
The C++ libraries boost (cf. http://www.boost.org/). Used are some functions for statistical distributions from the Boost Math Toolkit. Furthermore, the boost libraries are used for random number generation.
The nonlinear optimization library NLopt (http://ab-initio.mit.edu/wiki/index.php/NLopt).
OpenMP for parallel computing (http://openmp.org/wp/).
The Fortran 77 routine MVTDST (file mvtdstpack.f) from (http://www.math.wsu.edu/faculty/genz/software/software.html; Alan Genz). (It is only needed for computing the CDF of the bivariate normal and t copula.)

Installation under linux

With the following code you can check whether you have installed the needed software (C++ compiler, boost libraries, NLopt and OpenMP) on your UNIX system.

$ dpkg -s g++ | grep 'Version'
4:4.8.2-1ubuntu6
$ dpkg -s libboost-all-dev | grep 'Version'
1.54.0.1ubuntu1
$ dpkg -s libnlopt-dev | grep 'Version'
2.4.1+dfsg-1ubuntu1
dpkg -s libgomp1 | grep 'Version'
4.8.2-19ubuntu1

Getting started / Setting up the VineCopulaMatlab toolbox

Check whether you have a compatible C++ compiler (cf. http://www.mathworks.de/support/compilers for information about compatible compilers for different releases and operating systems). By the command mex -setup the compiler can be set.
Install the C++ libraries boost (cf. http://www.boost.org/).

$ apt-get install libboost-all-dev
Install the nonlinear optimization library NLopt (http://ab-initio.mit.edu/wiki/index.php/NLopt).

$ apt-get install libnlopt-dev
Optionally parallel computing with OpenMP can be used. Note that your C++ compiler needs to support parallel computing with OpenMP.

$ apt-get install libgomp1
Change the path to the directory you have saved the VineCopulaMatlab folder, i.e.,

$ cd [path]/VineCopulaMatlab
Build the VineCopulaMatlab toolbox using the make command.

$ make
Install the VineCopulaMatlab toolbox using the make command.

$ sudo make install

Installation under Windows

Setting up a C++ build environment for Windows

A nice step-by-step guidance can be found at ascend4.org. Following the steps for setting up MSYS, Switchable 32- and 64-bit modes and MinGW-w64 you will get a GCC (TDM-GCC MinGW-w64) for Windows including a command window. It also includes OpenMP for parallel computing.

Getting started / Setting up the VineCopulaMatlab toolbox

Download the mex_C++_mingw-w64.xml file from Chappjc's GitHub folder MATLAB. Open the file and add -fopenmp -lgfortran -lgomp -lnlopt -lVineCopulaCPP at the end of line 67 (definition of the variable LINKLIBS) directly after -lmex -lmx -leng -lmat -lmwlapack -lmwblas. Save the file mex_C++_mingw-w64.xml in the private folder of the VineCopulaMatlab toolbox.
The C++ libraries boost (cf. http://www.boost.org/) don't need to be build. Just add the include directory when building the toolbox.
The nonlinear optimization library NLopt. If you have set up MingGW-w64 you can just follow the installation guidance, i.e.,
```
./configure --prefix=Installation_Dir
make
make install
```
Build the VineCopulaMatlab toolbox using the make command. You may have to set the boostincludedir option. If the boost libraries files are in the folder C:\BoostDir, then you may set boostincludedir to /c/BoostDir.

$ make boostincludedir=/c/BoostDir
Install the VineCopulaMatlab toolbox using the make command. Depending on the installation directory (set as prefix) you may need to run msys as admin. You may have to set the prefix and boostincludedir options. If you set prefix to /c/Dir the library files will be placed in C:\Dir\lib and the include files will be placed in C:\Dir\include. Additionally you may want to specify the variables mingwroot (default value = "C:\MinGW\64") and mingw_mex_xml (default value = "mex_C++_mingw-w64.xml"), which specify where you have installed MinGW-w64 and how your mex setup file is called.

$ make install prefix=/c/Dir boostincludedir=/c/BoostDir

Remarks

For some C++ compilers the function expm1 is not available. A possible workaround is to use the expm1 from the boost libraries. Therefore, add the line #include <boost/math/special_functions/expm1.hpp> to the header file VineCopulaCPP_header.hpp. Then substitute expm1 by boost::math::expm1.
The function lgamma is also not available in some C++ compilers. A possible workaround is to use the lgamma function from the boost libraries. Therefore, add the line #include <boost/math/special_functions/gamma.hpp> to the header file VineCopulaCPP_header.hpp. Then substitute lgamma by boost::math::lgamma.

Handling of seeds for random number generation

Boost.Random is used for random number generation in C++ (cf. http://www.boost.org/doc/libs/1_56_0/doc/html/boost_random.html). If you execute installVineCPP there will be generated some seed for random number generation. With the function SetSeedVineCPP you can always set a new one. Further note:

The seed is saved in the file "/private/Seed.dat".
The seed is generate using the current system time.
If you want to save the seed for future usage, please make a copy of the file "Seed.dat". Every time random numbers are generated in C++ the file will be overwritten.
If the Seed should be set back to some former state, you just have to replace the "Seed.dat" file in "/private/" by your copy of Seed.dat you have previously generated.
For further information please visit: http://www.boost.org/doc/libs/1_56_0/doc/html/boost_random/reference.html

Parallel computing within the VineCopulaMatlab toolbox using OpenMP

The following functions can be (partly) computed in parallel:

PairCopulaSelect
StructureSelect(VineCopula)
Fit(VineCopula)
VineCopulaFit
VineCopulaNegLL

For effective usage of the parallelization you should enable nested parallel regions by:

export OMP_NESTED=TRUE

The maximum number (N) of threads generated can be set via:

export OMP_NESTED=TRUE