Title: Statistical inference on populations of weighted or unweighted
Author: Cedric E Ginestet <cedric.ginestet@kcl.ac.uk>, Andrew Simmons
Maintainer: Cedric E Ginestet <cedric.ginestet@kcl.ac.uk>
Depends: R (>= 2.10.1), nlme, lme4, igraph
Description: Network analysis is a recently adopted phrase, which
reflects the growing utilization of graph theory for the
analysis of a range of different types of connectivity data.
The set of objects contained in this package will particularly
useful for the analysis of population of networks in
neuroimaging, where subject-specific networks are available.
Future versions will include an SPN (statistical parametric
network) object, which permits to summarize one or several

Author: Cedric E Ginestet <cedric.ginestet@kcl.ac.uk>

Depends: R (>= 2.10.1), Rcpp (>= 0.8.9), nlme, lme4, igraph, inline
Description: Network analysis refers to the utilization of graph theory
for the analysis of a range of different types of connectivity
data. The set of objects contained in this package will be
particularly useful for the analysis of groups of networks in
neuroimaging, where several populations of subject-specific
networks are available. Future versions will include the
derivation of SPNs (statistical parametric networks). This
revised version of the package now includes C++ codes, which
permit to speed up the computational cost of the main

Title: Bayesian Package for Network Changepoint Analysis
Imports: ggplot2, Rmpfr, abind, mvtnorm, grid, LaplacesDemon, MCMCpack,
Description: Network changepoint analysis for undirected network data. The package implements a hidden Markov multilinear tenstor regression model (Park and Sohn, 2017, <http://jhp.snu.ac.kr/NetworkChange.pdf>). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided.
Packaged: 2017-03-29 18:13:48 UTC; interstellar

Packaged: 2017-05-25 18:29:41 UTC; interstellar

Author: Jong Hee Park [aut,cre], Yunkyu Sohn [aut]
stats, MASS, methods, RColorBrewer, ggvis
Description: Network changepoint analysis for undirected network data. The package implements a hidden Markov multilinear tensor regression model (Park and Sohn, 2017, <http://jhp.snu.ac.kr/NetworkChange.pdf>). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided.
Packaged: 2017-08-19 15:28:22 UTC; interstellar