pkgsrc-wip/R-bnlearn/distinfo

$NetBSD: distinfo,v 1.7 2015/03/02 14:31:25 bubuchka Exp $

SHA1 (R/bnlearn_4.0.tar.gz) = 6e6a28ddf6b24d4d7d5939e9764ae57c3dcf2e8a
RMD160 (R/bnlearn_4.0.tar.gz) = eaeb4ab81fa4d6a827febfcd0b921a360d51bac8
SHA512 (R/bnlearn_4.0.tar.gz) = 091d20ccbb3c570b044e66c15e88bf00922c21ad2330fefe57c9c785d532497dd9e90760b7719fd5e05090b32a9cacd6cfe4d536120a407dc45c77cf321e06ff
Size (R/bnlearn_4.0.tar.gz) = 1211527 bytes
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(). 2015-03-02 14:31:25 +00:00			$NetBSD: distinfo,v 1.7 2015/03/02 14:31:25 bubuchka Exp $
Import R-bnlearn-2.9 as wip/R-bnlearn. Bayesian network structure learning (via constraint-based, score-based and hybrid algorithms), parameter learning (via ML and Bayesian estimators) and inference. 2012-05-10 10:59:38 +00:00
R-bnlearn: Fix make-fetch: Update to 4.0 2016-09-20 21:08:07 +00:00			`SHA1 (R/bnlearn_4.0.tar.gz) = 6e6a28ddf6b24d4d7d5939e9764ae57c3dcf2e8a`
			`RMD160 (R/bnlearn_4.0.tar.gz) = eaeb4ab81fa4d6a827febfcd0b921a360d51bac8`
			`SHA512 (R/bnlearn_4.0.tar.gz) = 091d20ccbb3c570b044e66c15e88bf00922c21ad2330fefe57c9c785d532497dd9e90760b7719fd5e05090b32a9cacd6cfe4d536120a407dc45c77cf321e06ff`
			`Size (R/bnlearn_4.0.tar.gz) = 1211527 bytes`