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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: Makefile,v 1.13 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
CATEGORIES= math
MASTER_SITES= ${MASTER_SITE_R_CRAN:=contrib/}
Take responsibility for all that R stuff I imported so far.
2012-07-17 23:43:43 +00:00
MAINTAINER= mishka@NetBSD.org
all: migrate homepages from http to https pkglint -r -F --only "migrate from" In addition, pkglint fixed some indentation in the affected files. These fixes have been checked manually to be fine. They all were, except for the continuation backslashes from wkhtmltopdf/Makefile, which have been reverted manually.
2020-01-27 19:56:27 +00:00
HOMEPAGE= https://www.bnlearn.com/
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
COMMENT= Bayesian network structure learning, parameter learning and inference
Move LICENSE right below COMMENT, that's where it usually should be. Noted by <wiz> - thank you very much!
2013-03-19 01:22:55 +00:00
LICENSE= gnu-gpl-v2 OR gnu-gpl-v3
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_PKGNAME= bnlearn
R-bnlearn: Fix make-fetch: Update to 4.0
2016-09-20 21:08:07 +00:00
R_PKGVER= 4.0
All BLAS dependents: Manual rev bump
2020-10-12 22:07:16 +00:00
PKGREVISION= 1
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
Fix spacing.
2012-08-28 15:39:17 +00:00
USE_LANGUAGES= c fortran
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
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
BUILDLINK_API_DEPENDS.R+= R>=2.14.0
Cleanup Makefiles and get them ready to import.
2012-08-30 19:03:42 +00:00
Align .includes.
2012-07-18 11:34:48 +00:00
.include "../../math/R/Makefile.extension"
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
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
.include "../../devel/gettext-lib/buildlink3.mk"
All BLAS dependents: Update .include to use new mk/blas.buildlink3.mk
2020-10-12 22:01:38 +00:00
.include "../../mk/blas.buildlink3.mk"
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
.include "../../mk/bsd.pkg.mk"