R-VGAM-1.1.1.tgz


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Description

R-VGAM - Vector generalized linear and additive models

Property Value
Distribution NetBSD 7.2
Repository NetBSD amd64
Package filename R-VGAM-1.1.1.tgz
Package name R-VGAM
Package version 1.1.1
Package release -
Package architecture amd64
Package type tgz
Category R math
Homepage https://CRAN.R-project.org/package=VGAM
License gnu-gpl-v3
Maintainer -
Download size 7.32 MB
Installed size 8.34 MB
Vector generalized linear and additive models, and associated models
(Reduced-Rank VGLMs, Quadratic RR-VGLMs, Reduced-Rank VGAMs). This
package fits many models and distribution by maximum likelihood
estimation (MLE) or penalized MLE. Also fits constrained ordination
models in ecology.

Alternatives

Package Version Architecture Repository
R-VGAM-1.1.1.tgz 1.1.1 i386 NetBSD
R-VGAM - - -

Requires

Name Value
R >= 2.2.1nb2
g95 >= 0.91

Download

Type URL
Mirror ftp.netbsd.org
Binary Package R-VGAM-1.1.1.tgz
Source Package R-VGAM

Install Howto

Install R-VGAM tgz package:

# pkg_add R-VGAM

Files

Path
/usr/pkg/lib/R/library/VGAM/libs/VGAM.so

See Also

Package Description
R-XML-3.98.1.20.tgz Tools for parsing and generating XML within R
R-abind-1.4.5.tgz Combine multi-dimensional arrays
R-acepack-1.4.1.tgz ACE and AVAS for selecting multiple regression transformations
R-akima-0.6.2.tgz Linear or cubic spline interpolation for irregular gridded data
R-aplpack-1.3.2.tgz Functions for drawing special plots
R-askpass-1.1.tgz Safe password entry for R, Git, and SSH
R-assertthat-0.2.1.tgz Easy pre and post assertions
R-backports-1.1.4.tgz Reimplementations of functions introduced since R-3.0.0
R-base64enc-0.1.3.tgz Tools for base64 encoding
R-bayesm-3.1.3.tgz Bayesian inference for marketing/micro-econometrics
R-bbmle-1.0.20.tgz Tools for general maximum likelihood estimation
R-bindr-0.1.1.tgz Parametrized Active Bindings
R-bit-1.1.14.tgz Class for vectors of 1-bit booleans
R-bitops-1.0.6.tgz Functions for Bitwise operations on integer vectors
R-blob-1.2.0.tgz Simple S3 class for representing vectors of binary data ('BLOBS')
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