Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
R Minor changes Sep 20, 2018
data Updated Mar 15, 2017
inst Minor changes Sep 19, 2018
man Minor changes Sep 20, 2018
src Minor changes Sep 20, 2018
tests
vignettes
.DS_Store Minor changes Sep 19, 2018
.Rbuildignore Updated Jun 13, 2017
.gitignore Up Jul 11, 2017
.travis.yml Some update Jul 5, 2017
DESCRIPTION Minor changes Sep 20, 2018
NAMESPACE Updated Aug 28, 2018
README.md Minor changes Sep 19, 2018
appveyor.yml Up Jul 11, 2017

README.md

PAFit package

codecov Downloads from CRAN Downloads from CRAN CRAN License: GPL v3

This package provides a framework for modelling and inferencing attachment mechanisms of temporal complex networks. For estimating the preferential attachment (PA) function in isolation, we implement Jeong's method, the corrected Newman's method and the PAFit method. For jointly estimating the PA function and node fitnesses, we implement the PAFit method. The package can quantify the remaining uncertainties by providing confidence intervals for the estimated results. We also provide flexible methods to generate a wide range of temporal networks based on PA and fitness.

Installation

The release version of the package is hosted on CRAN and can be installed in the usual way:

install.packages("PAFit")

This dev version on GitHub can be installed as follows:

require(devtools)
install_github("thongphamthe/PAFit@devel")

Getting started

To get started, load the package

library("PAFit")

then work through the tutorial (link to the current CRAN version):

Reference manual

Please refer to the current version on CRAN:

You can view the html version, which has a better layout but renders mathematical symbols worse than the pdf version, if you use Rstudio

NEWS

Please refer to the current version on CRAN:

Citation

Please refer to the citation information file (link to the current CRAN version):

License

GPL-3

Other information

  • If you have any suggestions or find bugs, please use the github issue tracker
  • Feel free to submit pull requests