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math/R-forecast,
Forecasting functions for time series and linear models
Branch: CURRENT,
Version: 8.7,
Package name: R-forecast-8.7,
Maintainer: minskimMethods and tools for displaying and analysing univariate time series
forecasts including exponential smoothing via state space models and
automatic ARIMA modelling.
Required to run:[
lang/g95] [
math/R] [
math/R-zoo] [
devel/R-Rcpp] [
devel/R-magrittr] [
math/R-fracdiff] [
graphics/R-colorspace] [
time/R-timeDate] [
finance/R-tseries] [
graphics/R-ggplot2] [
math/R-lmtest] [
math/R-RcppArmadillo] [
math/R-urca]
Required to build:[
pkgtools/cwrappers]
Master sites: (Expand)
Version history: (Expand)
- (2019-08-01) Updated to version: R-forecast-8.7
- (2018-03-23) Package added to pkgsrc.se, version R-forecast-8.2 (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.
|
2018-03-23 17:43:28 by Min Sik Kim | Files touched by this commit (3) |
Log message:
math/R-forecast: Import version 8.2
Methods and tools for displaying and analysing univariate time series
forecasts including exponential smoothing via state space models and
automatic ARIMA modelling.
|