GitLab

Compatibility fix for Octave: under Octave, union() called on a column vector and a scalar returns a row vector, hence the need to add the 'rows' option

Estimation DLL: fix DecisionRules::compute() (was not reentrant, since GeneralizedSchurDecomposition::compute() modifies D and E)

Preprocessor: no longer use boost::graph::GraphvizDigraph class, removed in Boost 1.44 (closes #155)

changed variable name to make clear that, for multinormal variables, we are using the Cholesky decomposition of the covariance matrix.

- Bug correction during the block decomposition: the models' graph was ill oriented before the search of strong components

added choice of parameter set to shock_decomposition. This corrects a bug when shock_decomposition is requested after Metropolis iterations.

fixed bug that would force node sharing when two or more expectation operators had the same expression but different information sets

NaN parameters warning: remove \n, it is not interpreted in a warning context (at least under Octave)

C++ Estimation DLL: Update of logMHMCMCposterior.cc with a draft octave MAT draws file save and adding a test random_walk_metropolis_hastings_core.m: Octave version crashes at start of DLL and Matlab version finishes with low acceptance due to frequent B&K and reports error within debugger too - needs more debugging!

remove get_innovation_contemporaneous_impact.m that isn't called by the preprocessor or  by any other function

Fixed bug in resid.m (order matters in "or" statement, because of short-circuit: "ind" is not defined if "istag" is false)

Bugfix at order 3 (hopefully was not affecting final results, thanks to Pablo Winant for spotting this):
- in the 3rd derivatives matrix, among symmetric elements, the first one had
the right values but the following ones were set to zero
- moreover, the k-order DLL was trying to add all the symmetric elements in the
folded tensor, instead of only keeping one value among all the symmetric ones
- hopefully, Ondra's tensor library was (silently) refusing to add symmetric
elements after the first (and right) value had been added
- so the final result was correct

Posterior distribution of conditional variance decomposition: storing the steps chosen by the user in a separate field.