A statistically sound method for estimating jointly the attachment function and node fitness in a temporal complex network by maximizing a suitable penalized log-likelihood function is implemented in this package. Tutorial can be found in the accompanying vignette. For a list of references, please run the command: citation("PAFit").
Rcpp (>= 0.11.3) , grDevices, graphics, stats, RColorBrewer,
VGAM

Joint Inference of Preferential Attachment and Node Fitness in
Temporal Complex Networks
A framework for modelling and inferencing the 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.

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.

Modelling and Inferencing Attachment Mechanisms of Temporal
Complex Networks
Rcpp (>= 0.11.3) , grDevices, graphics, stats, RColorBrewer,
VGAM, MASS, magicaxis

Generative Mechanism Estimation in Temporal Complex Networks
Statistical methods for estimating preferential attachment and node fitness generative mechanisms in temporal complex networks are provided.

Rcpp (>= 0.11.3) , grDevices, graphics, stats, RColorBrewer,
VGAM, MASS, magicaxis, networkDynamic, network, plyr, igraph

Rcpp (>= 0.11.3) , grDevices, graphics, stats, RColorBrewer,
VGAM, MASS, magicaxis, networkDynamic, network, plyr, igraph,
mapproj