parents and leaves is rename to jt_parents and jt_leaves respectively no avoid nameclashes with bnlearn.jt_nbinary_ops and new triangulation methods.jt_binary_ops breaking api slightly. The algorithm is now much faster though.compile gets a new argumen initialize_cpts in order to speed up computations when insertion of evidence at CPT level are of interest.initialize to initialize an object from compile if initialize_cpts was set to FALSEpot_list deprecated. Use cpt_list for both BNs and MRFsjt_nbinary_ops more than twice as fast.pot_list for markov random fields which is more efficient and idiomaticjoint_vars is specified, the root node is automatically set to the clique where these variables are located. Hence, one only needs to collect to query probabilies about these variablesjt algorithm now proceeds assuming a uniform prior distribution
on the affected tables. In this regard, one can not query the evidence since it has no meaning. The print method flags
if there are inconsistencies and it can be obtained by has_inconsistencies. This new feature means, that jti can
now also be seen as a "machine-learning" algorithm that can be very useful in connection with class-prediction e.g.new_mpd renamed to mpd. Works on cpt_list objects now.triangulate method for cpt_list objects.evidence, for triangulation.jt_binary_ops for calculating the number of binary operations to perform a full message passingmpd for finding maximal prime decompositions is now include includedtriangulate a graph before compilation in order to investigate the size of the cliques etc.There was a bug in the creation of the junction tree when calling Kruskals algorithm.
It is now possible to specify variables of interest in advance, such that we are guaranteed to be able to query the joint pmf of these variables.
Some refactoring making compilation much faster. When potentials is assigned to a clique we no longer start by creating a unity table and then multiply. This was killing the advantage of the sparsity.