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- Add new port: math/R-cran-ddalpha

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Contains procedures for depth-based supervised learning, which are
  entirely non-parametric, in particular the DDalpha-procedure (Lange,
  Mosler and Mozharovskyi, 2014). The training data sample is transformed
  by a statistical depth function to a compact low-dimensional space,
  where the final classification is done. It also offers an extension
  to functional data and routines for calculating certain notions of
  statistical depth functions. 50 multivariate and 5 functional
  classification problems are included.

  WWW: https://cran.r-project.org/web/packages/ddalpha/
This commit is contained in:
TAKATSU Tomonari 2017-09-10 12:08:13 +00:00
parent 902d920995
commit 548c1fcc31
Notes: svn2git 2021-03-31 03:12:20 +00:00 
svn path=/head/; revision=449539
4 changed files with 36 additions and 0 deletions
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1
math/Makefile
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@ -36,6 +36,7 @@
SUBDIR += R-cran-car
SUBDIR += R-cran-coda
SUBDIR += R-cran-combinat
SUBDIR += R-cran-ddalpha
SUBDIR += R-cran-deldir
SUBDIR += R-cran-dimRed
SUBDIR += R-cran-dlmodeler

22
math/R-cran-ddalpha/Makefile Normal file
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@ -0,0 +1,22 @@
# Created by: TAKATSU Tomonari <tota@FreeBSD.org>
# $FreeBSD$
PORTNAME= ddalpha
PORTVERSION= 1.2.1
CATEGORIES= math
DISTNAME= ${PORTNAME}_${PORTVERSION}
MAINTAINER= tota@FreeBSD.org
COMMENT= Depth-Based Classification and Calculation of Data Depth
LICENSE= GPLv2
CRAN_DEPENDS= R-cran-robustbase>0:math/R-cran-robustbase \
R-cran-Rcpp>=0.11.0:devel/R-cran-Rcpp \
R-cran-BH>0:devel/R-cran-BH
BUILD_DEPENDS= ${CRAN_DEPENDS}
RUN_DEPENDS= ${CRAN_DEPENDS}
USES= cran:auto-plist,compiles
.include <bsd.port.mk>

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math/R-cran-ddalpha/distinfo Normal file
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TIMESTAMP = 1505038914
SHA256 (ddalpha_1.2.1.tar.gz) = c4a9842e9dec1bd992460e6c68b5f2f5d6463d0c2474ef2e0e572dd34b284d5b
SIZE (ddalpha_1.2.1.tar.gz) = 417557

10
math/R-cran-ddalpha/pkg-descr Normal file
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Contains procedures for depth-based supervised learning, which are
entirely non-parametric, in particular the DDalpha-procedure (Lange,
Mosler and Mozharovskyi, 2014). The training data sample is transformed
by a statistical depth function to a compact low-dimensional space,
where the final classification is done. It also offers an extension
to functional data and routines for calculating certain notions of
statistical depth functions. 50 multivariate and 5 functional
classification problems are included.
WWW: https://cran.r-project.org/web/packages/ddalpha/