R-cran-nnls - R implementation of an algorithm for non-negative least squares

Property Value
Distribution FreeBSD 10
Repository FreeBSD Ports Latest amd64
Package name R-cran-nnls
Package version 1.4
Package release 9
Package architecture amd64
Package type txz
Installed size 59.24 KB
Download size 38.52 KB
Official Mirror pkg.freebsd.org
An R interface to the Lawson-Hanson implementation of an algorithm for
non-negative least squares (NNLS). Also allows the combination of non-negative
and non-positive constraints.
WWW: http://cran.r-project.org/web/packages/nnls/
Categories: math
Maintainer: skreuzer@FreeBSD.org


Package Version Architecture Repository
R-cran-nnls-1.4_9.txz 1.4 i386 FreeBSD Ports Quarterly
R-cran-nnls-1.4_9.txz 1.4 amd64 FreeBSD Ports Quarterly
R-cran-nnls-1.4_9.txz 1.4 i386 FreeBSD Ports Latest
R-cran-nnls - - -


Name Value
R = 3.5.1_1
gcc7 = 7.3.0_5
libR.so.3.5 -
libgfortran.so.4 -
libquadmath.so.0 -


Type URL
Binary Package R-cran-nnls-1.4_9.txz
Source Package math/R-cran-nnls

Install Howto

Install R-cran-nnls txz package:

# pkg install R-cran-nnls

See Also

Package Description
R-cran-nortest-1.0.4_1.txz Tests for Normality
R-cran-numDeriv-2016.8.1_1.txz Accurate Numerical Derivatives
R-cran-numbers-0.7.1.txz Number-Theoretic Functions
R-cran-openssl-1.0.2.txz Toolkit for Encryption, Signatures and Certificates Based on OpenSSL
R-cran-openxlsx-4.1.0.txz Read, Write and Edit XLSX Files
R-cran-outliers-0.14_6.txz Collection of some tests commonly used for identifying outliers
R-cran-partitions-1.9.19.txz Additive Partitions of Integers
R-cran-pbkrtest-0.4.7_1.txz Parametric bootstrap and Kenward-Roger-based methods for mixed model comparison
R-cran-pillar-1.3.0.txz Coloured Formatting for Columns
R-cran-pixmap-0.4.11_11.txz Bitmap Images ("Pixel Maps")
R-cran-pkgconfig-2.0.2.txz Private Configuration for 'R' Packages
R-cran-pkgmaker-0.27.txz Package development utilities
R-cran-plm-1.6.6.txz Linear Models for Panel Data
R-cran-plogr-0.2.0.txz Plog C++ Logging Library
R-cran-pls-2.7.0.txz Partial Least Squares and Principal Component Regression