Log Message:
Update R-bnlearn to version 3.7.1.

Changes:

bnlearn (3.7.1)

  * small changes to make CRAN check happy.

bnlearn (3.7)

  * fixed the default setting for the number of particles in cpquery()
     (thanks Nishanth Upadhyaya).
  * reimplemented common test patterns in monolithic C functions to speed
     up constraint-based algorithms.
  * added support for conditional linear Gaussian (CLG) networks.
  * fixed several recursion bugs in choose.direction().
  * make read.{bif,dsc,net}() consistent with the `$<-` method for bn.fit
     objects (thanks Felix Rios).
  * support empty networks in read.{bif,dsc,net}().
  * fixed bug in hc(), triggered when using both random restarts and the
     maxp argument (thanks Irene Kaplow).
  * correctly initialize the Castelo & Siebes prior (thanks Irene Kaplow).
  * change the prior distribution for the training variable in classifiers
     from the uniform prior to the fitted distribution in the
     bn.fit.{naive,tan} object, for consistency with gRain and e1071 (thanks
     Bojan Mihaljevic).
  * note AIC and BIC scaling in the documentation (thanks Thomas Lefevre).
  * note limitations of {white,black}lists in tree.bayes() (thanks Bojan
     Mihaljevic).
  * better input sanitization in custom.fit() and bn.fit<-().
  * fixed .Call stack imbalance in random restarts (thanks James Jensen).
  * note limitations of predict()ing from bn objects (thanks Florian Sieck).

bnlearn (3.6)

  * support rectangular nodes in {graphviz,strength}.plot().
  * fixed bug in hc(), random restarts occasionally introduced cycles in
     the graph (thanks Boris Freydin).
  * handle ordinal networks in as.grain(), treat variables as categorical
     (thanks Yannis Haralambous).
  * discretize() returns unordered factors for backward compatibility.
  * added write.dot() to export network structures as DOT files.
  * added mutual information and X^2 tests with adjusted degrees of freedom.
  * default vstruct() and cpdag() to moral = FALSE (thanks Jean-Baptiste
     Denis).
  * implemented posterior predictions in predict() using likelihood weighting.
  * prevent silent reuse of AIC penalization coefficient when computing BIC
     and vice versa (thanks MarГ­a Luisa Matey).
  * added a "bn.cpdist" class and a "method" attribute to \ 
the random data
     generated by cpdist().
  * attach the weights to the return value of cpdist(..., method = "lw").
  * changed the default number of simulations in cp{query, dist}().
  * support interval and multiple-valued evidence for likelihood weighting
     in cp{query,dist}().
  * implemented dedup() to pre-process continuous data.
  * fixed a scalability bug in blacklist sanitization (thanks Dong Yeon Cho).
  * fixed permutation test support in relevant().
  * reimplemented the conditional.test() backend completely in C for
     speed, it is now called indep.test().