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pkgsrc/math/ranger/Makefile

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math: align variable assignments pkglint -Wall -F --only aligned -r Manual correction in R/Makefile.extension for the MASTER_SITES continuation line.
2019-11-02 17:16:18 +01:00
# $NetBSD: Makefile,v 1.3 2019/11/02 16:16:22 rillig Exp $
Importing math/ranger Ranger is a fast implementation of random forest (Breiman 2001) or recursive partitioning, particularly suited for high dimensional data. Classification, regression, probability estimation and survival forests are supported. Classification and regression forests are implemented as in the original Random Forest (Breiman 2001), survival forests as in Random Survival Forests (Ishwaran et al. 2008). For probability estimation forests see Malley et al. (2012).
2016-08-19 22:24:36 +02:00
DISTNAME= ranger-0.4.2
CATEGORIES= math
MASTER_SITES= ${MASTER_SITE_GITHUB:=imbs-hl/}
MAINTAINER= cheusov@NetBSD.org
HOMEPAGE= https://github.com/imbs-hl/ranger
COMMENT= Fast Implementation of Random Forests
LICENSE= gnu-gpl-v3
math: align variable assignments pkglint -Wall -F --only aligned -r Manual correction in R/Makefile.extension for the MASTER_SITES continuation line.
2019-11-02 17:16:18 +01:00
WRKSRC= ${WRKDIR}/${DISTNAME}/source
USE_LANGUAGES= c c++
USE_CMAKE= yes
PTHREAD_AUTO_VARS= yes
Importing math/ranger Ranger is a fast implementation of random forest (Breiman 2001) or recursive partitioning, particularly suited for high dimensional data. Classification, regression, probability estimation and survival forests are supported. Classification and regression forests are implemented as in the original Random Forest (Breiman 2001), survival forests as in Random Survival Forests (Ishwaran et al. 2008). For probability estimation forests see Malley et al. (2012).
2016-08-19 22:24:36 +02:00
INSTALLATION_DIRS+= bin
do-install:
${INSTALL_PROGRAM} ${WRKSRC}/ranger ${DESTDIR}${PREFIX}/bin
.include "../../devel/zlib/buildlink3.mk"
Fix pthread use.
2016-08-26 19:17:22 +02:00
.include "../../mk/pthread.buildlink3.mk"
Importing math/ranger Ranger is a fast implementation of random forest (Breiman 2001) or recursive partitioning, particularly suited for high dimensional data. Classification, regression, probability estimation and survival forests are supported. Classification and regression forests are implemented as in the original Random Forest (Breiman 2001), survival forests as in Random Survival Forests (Ishwaran et al. 2008). For probability estimation forests see Malley et al. (2012).
2016-08-19 22:24:36 +02:00
.include "../../mk/bsd.pkg.mk"
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