This package is considered a duplicate. The official version of this package is found at:https://thongphamthe.r-universe.dev/PAFit

Package: PAFit 1.2.10

Thong Pham

PAFit: Generative Mechanism Estimation in Temporal Complex Networks

Statistical methods for estimating preferential attachment and node fitness generative mechanisms in temporal complex networks are provided. Thong Pham et al. (2015) <doi:10.1371/journal.pone.0137796>. Thong Pham et al. (2016) <doi:10.1038/srep32558>. Thong Pham et al. (2020) <doi:10.18637/jss.v092.i03>. Thong Pham et al. (2021) <doi:10.1093/comnet/cnab024>.

Authors:Thong Pham, Paul Sheridan, Hidetoshi Shimodaira

PAFit_1.2.10.tar.gz
PAFit_1.2.10.tar.gz(r-4.5-noble)PAFit_1.2.10.tar.gz(r-4.4-noble)
PAFit_1.2.10.tgz(r-4.4-emscripten)PAFit_1.2.10.tgz(r-4.3-emscripten)
PAFit.pdf |PAFit.html✨
PAFit/json (API)
NEWS

# Install 'PAFit' in R:

install.packages('PAFit', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/thongphamthe/pafit/issues

Uses libs:

c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

Datasets:

coauthor.author_id - A collaboration network between authors of papers in the field of complex networks with article time-stamps
coauthor.net - A collaboration network between authors of papers in the field of complex networks with article time-stamps
coauthor.truetime - A collaboration network between authors of papers in the field of complex networks with article time-stamps

cpp openmp

3.54 score 70 scripts 437 downloads 5 mentions 22 exports 55 dependencies

Last updated 9 months agofrom:5d111ba8b1. Checks:OK: 1 NOTE: 1. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Dec 24 2024
R-4.5-linux-x86_64	NOTE	Dec 24 2024

Exports:as.PAFit_net from_igraph from_networkDynamic generate_BA generate_BB generate_ER generate_fit_only generate_net generate_simulated_data_from_estimated_model get_statistics graph_from_file graph_to_file Jeong joint_estimate Newman only_A_estimate only_F_estimate PAFit_oneshot plot_contribution test_linear_PA to_igraph to_networkDynamic

Dependencies:celestial cli coda colorspace cpp11 dplyr evaluate fansi farver generics ggplot2 glue gtable highr igraph isoband knitr labeling lattice lifecycle magicaxis magrittr mapproj maps MASS Matrix mgcv munsell network networkDynamic networkLite NISTunits nlme pillar pkgconfig plotrix plyr pracma R6 RANN RColorBrewer Rcpp rlang scales sm statnet.common tibble tidyselect utf8 vctrs VGAM viridisLite withr xfun yaml

PAFit: an R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks

Rendered fromTutorial.pdf.asisusingR.rsp::asison Dec 24 2024.

Last update: 2018-09-19
Started: 2015-03-27

Citation

Readme and manuals

Help Manual

Help page	Topics
Generative Mechanism Estimation in Temporal Complex Networks	PAFit-package PAFit
Converting an edgelist matrix to a PAFit_net object	as.PAFit_net
A collaboration network between authors of papers in the field of complex networks with article time-stamps	coauthor.author_id coauthor.net coauthor.truetime ComplexNetCoauthor
Convert an igraph object to a PAFit_net object	from_igraph
Convert a networkDynamic object to a PAFit_net object	from_networkDynamic
Simulating networks from the generalized Barabasi-Albert model	generate_BA
Simulating networks from the Bianconi-Barabasi model	generate_BB
Simulating networks from the Erdos-Renyi model	generate_ER
Simulating networks from the Caldarelli model	generate_fit_only
Simulating networks from preferential attachment and fitness mechanisms	generate_net
Generating simulated data from a fitted model	generate_simulated_data_from_estimated_model
Getting summarized statistics from input data	get_statistics PAFit_data
Read file to a PAFit_net object	graph_from_file
Write the graph in a PAFit_net object to file	graph_to_file
Jeong's method for estimating the preferential attachment function	Jeong
Joint inference of attachment function and node fitnesses	joint_estimate
Corrected Newman's method for estimating the preferential attachment function	Newman
Estimating the attachment function in isolation by PAFit method	only_A_estimate
Estimating node fitnesses in isolation	only_F_estimate
Estimating the nonparametric preferential attachment function from one single snapshot.	PAFit_oneshot
Plotting contributions calculated from the observed data and contributions calculated from simulated data	plot_contribution
Plotting the estimated attachment function and node fitness	plot.Full_PAFit_result
Plotting the estimated attachment function	plot.PA_result
Plot a 'PAFit_net' object	plot.PAFit_net
Plotting the estimated attachment function and node fitness of a 'PAFit_result' object	plot.PAFit_result
Printing simple information of the cross-validation data	print.CV_Data
Printing simple information of the cross-validation result	print.CV_Result
printing information on the estimation result	print.Full_PAFit_result
Printing information of the estimated attachment function	print.PA_result
Printing simple information on the statistics of the network stored in a 'PAFit_data' object	print.PAFit_data
Printing simple information of a 'PAFit_net' object	print.PAFit_net
printing information on the estimation result stored in a 'PAFit_result' object	print.PAFit_result
Printing summary information of the cross-validation data	summary.CV_Data
Output summary information of the cross-validation result	summary.CV_Result
Summary information on the estimation result	summary.Full_PAFit_result
Summary of the estimated attachment function	summary.PA_result
Output summary information on the statistics of the network stored in a 'PAFit_data' object	summary.PAFit_data
Summary information of a 'PAFit_net' object	summary.PAFit_net
Output summary information on the estimation result stored in a 'PAFit_result' object	summary.PAFit_result
Fitting various distributions to a degree vector	test_linear_PA
Convert a PAFit_net object to an igraph object	to_igraph
Convert a PAFit_net object to a networkDynamic object	to_networkDynamic