BRANCH: created branch process

syncsvn: 2507
Added the DickeyFullerTest class.
This class enables to perform the Dickey Fuller test to check the stationarity of a time series.
Three models are usually investigated and a strategy may be launched thanks to runStrategy.
The class is not static comparing to other stat class tests due to the reuse of information while executing the strategy.  The constructor takes a time series as argument.  Intermediate tests of the strategy are visible.
The complete table of Dickey Fuller is not available. But asymptotic DickeyFuller quantile and cumulative functions were added. These are based on three values (0.01, 0.05 and 0.10) and a linear approximation. The final functions will be more efficient

syncsvn: 2517
Added test files for DickeyFullerTest class

syncsvn: 2518
Disabled DickeyFuller tests (library and python)

syncsvn: 2519
Added CovarianceModelFactoryImplementation and CovarianceModelFactory class

syncsvn: 2520
Added UserDefinedCovarianceModel class

syncsvn: 2521
Added NonStationaryCovarianceModelFactory class.
The class enables to estimate a covariance model using a sample of data and no particular hypothesis.

syncsvn: 2522
Rewrite NonStationaryCovarianceModelFactory::build to use less memory.
The initial version used an N*dimension auxiliary NumericalSample to compute its CovarianceMatrix.  It is more memory efficient to compute (N*dimension)*(N*dimension+1)/2 matrices.

syncsvn: 2523
Added missing tests for UserDefinedCovarianceModel class

syncsvn: 2524
Added missing tests for NonStationaryCovarianceModel class.
distcheck ok

syncsvn: 2525
Added the class UserDefinedStationaryCovarianceModel

syncsvn: 2526
Added library tests for UserDefinedStationaryCovarianceModel

syncsvn: 2527
Added the StationaryCovarianceModelFactory class.
This class enables to estimate a stationary covariance model using the spectral density information. An FFT inversion is used to estimate the covariance model.
The discretization is similar to the Fogli's one.

syncsvn: 2528
Added StationaryCovarianceModelFactory tests.
distcheck ok

syncsvn: 2529
Added the ARMALikelihoodFactory library class.
The class enables to estimate multivariate ARMA models using the likelihood function.
As the usual algorithm is based on the inversion of the covariance matrix, we use a more efficient algorithm (cf J.A. Mauricio).
For the optimization algorithm, default initial values are estimated using the WhittleFactory if user does not set ones.
The class returns an exception if both p and q are 0.
Added ARMALikelihoodFactory keys in ResourceMap.
The algorithm here consists in separating elementary tasks:
 - Compute the theoritical cross/auto covariance matrices. These are stored in  crossCovariance_ and autoCovariance_ attributes.
 - Assemble the information and covariance matrices (Omega, Sigma, V...)
 - Compute the cholesky factor and solve a linear system
 - Compute the objective value

The coefficientsBlockMatrix_ attribute stores the coefficients of the model. Care to the difference of conventions between Mauricio and OpenTurns ones.

syncsvn: 2530
Transpose and rename coefficientsBlockMatrix_ matrix.
Most matrix multiplications with phi and theta are matrices are:
  phi * B or B * phi^{T}
Since OpenTurns use a column storage, it is more efficient to compute
  M_{ij} = A_{ik} B_{kj}
         = (A^{T})_{ki} B_{kj}
This is why transposed matrices are stored, and coefficientsBlockMatrix_ is renamed into blockPhiTThetaTMatrix_.
For the same reason, switch some loops on rows and columns.
Fix several occurrences of (currentP_ - 1) which may cause trouble when currentP_ is 0.

syncsvn: 2531
Added a particular case of compute covariance method for pure AR models. This method enables to compute the covariance matrix using the white noise covariance matrix of size p.
Removed some useless boolean variables and added verbose accessors.
Renamed the logLikelihhod function and completed the method. This enables to compute the main factor of the likelihood function.
Completed the ComputeObjectiveAndConstraint method by setting the constraints on AR values, MA values and covariance matrix.
Completed the build method by launching the cobyla algorithm.
Made some cleaning and fixed some minor bugs.

syncsvn: 2532
Added ARMALikelihood library tests

syncsvn: 2533