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math/R-poweRlaw,
Analysis of heavy tailed distributions
Branch: CURRENT,
Version: 0.70.2,
Package name: R-poweRlaw-0.70.2,
Maintainer: minskimThe poweRlaw package is an implementation of maximum likelihood
estimators for a variety of heavy tailed distributions, including both
the discrete and continuous power law distributions. Additionally, a
goodness-of-fit based approach is used to estimate the lower cut-off
for the scaling region.
Required to run:[
math/R] [
math/R-VGAM]
Required to build:[
pkgtools/cwrappers]
Master sites: (Expand)
Version history: (Expand)
- (2019-08-01) Updated to version: R-poweRlaw-0.70.2
- (2018-03-03) Package added to pkgsrc.se, version R-poweRlaw-0.70.1 (created)
CVS history: (Expand)
2019-08-08 21:53:58 by Brook Milligan | Files touched by this commit (189) |  |
Log message:
Update all R packages to canonical form.
The canonical form [1] of an R package Makefile includes the
following:
- The first stanza includes R_PKGNAME, R_PKGVER, PKGREVISION (as
needed), and CATEGORIES.
- HOMEPAGE is not present but defined in math/R/Makefile.extension to
refer to the CRAN web page describing the package. Other relevant
web pages are often linked from there via the URL field.
This updates all current R packages to this form, which will make
regular updates _much_ easier, especially using pkgtools/R2pkg.
[1] http://mail-index.netbsd.org/tech-pkg/2 … 21711.html
|
2018-07-28 16:40:53 by Brook Milligan | Files touched by this commit (126) |
Log message:
Remove MASTER_SITES= from individual R package Makefiles.
Each R package should include ../../math/R/Makefile.extension, which also
defines MASTER_SITES. Consequently, it is redundant for the individual
packages to do the same. Package-specific definitions also prevent
redefining MASTER_SITES in a single common place.
|
2018-03-03 02:23:29 by Min Sik Kim | Files touched by this commit (3) |
Log message:
math/R-poweRlaw: Import version 0.70.1
The poweRlaw package is an implementation of maximum likelihood
estimators for a variety of heavy tailed distributions, including both
the discrete and continuous power law distributions. Additionally, a
goodness-of-fit based approach is used to estimate the lower cut-off
for the scaling region.
|