PAFit: Modelling and Inferencing Attachment Mechanisms of Temporal Complex Networks

A framework for modelling and inferencing attachment mechanisms of temporal complex networks is implemented in this package. 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 also provides flexible methods to generate a wide range of temporal networks based on PA and fitness.

Version: 0.9.8.3
Depends: R (≥ 2.10.0)
Imports: Rcpp (≥ 0.11.3) , grDevices, graphics, stats, RColorBrewer, VGAM, MASS, magicaxis
LinkingTo: Rcpp
Suggests: R.rsp
Published: 2017-03-13
Author: Thong Pham, Paul Sheridan, Hidetoshi Shimodaira
Maintainer: Thong Pham <thongpham at thongpham.net>
BugReports: https://github.com/thongphamthe/PAFit/issues
License: GPL-3
URL: https://github.com/thongphamthe/PAFit
NeedsCompilation: yes
Citation: PAFit citation info
Materials: NEWS
In views: SocialSciences
CRAN checks: PAFit results

Downloads:

Reference manual: PAFit.pdf
Vignettes: An Introduction to PAFit
Package source: PAFit_0.9.8.3.tar.gz
Windows binaries: r-devel: PAFit_0.9.8.3.zip, r-release: PAFit_0.9.8.3.zip, r-oldrel: PAFit_0.9.8.3.zip
OS X Mavericks binaries: r-release: PAFit_0.9.8.3.tgz, r-oldrel: PAFit_0.9.8.3.tgz
Old sources: PAFit archive

Reverse dependencies:

Reverse imports: mcPAFit

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