Depends: R (>= 2.10.0), MCMCpack, ggplot2, ggvis
Imports: grid, LaplacesDemon, Rmpfr, abind, mvtnorm, tidyr, stats,

Depends: R (>= 2.10.0), MCMCpack, ggplot2
Imports: grid, LaplacesDemon, Rmpfr, abind, mvtnorm, tidyr, sna,
igraph, qgraph, network, stats, MASS, methods, RColorBrewer,
reshape, ggrepel, gridExtra, rlang, GGally, ggvis
Description: Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn 2019). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided.

Imports: grid, Rmpfr, abind, mvtnorm, tidyr, sna, igraph, qgraph,
network, stats, MASS, methods, RColorBrewer, reshape, ggrepel,

Description: Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn 2020). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided.

Imports: grid, Rmpfr, abind, mvtnorm, tidyr, igraph, qgraph, network,
stats, MASS, methods, RColorBrewer, reshape, ggrepel,
Description: Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided.

Package: NetworkComparisonTest
Type: Package
Title: Statistical Comparison of Two Networks Based on Three Invariance
Measures
Version: 2.0.0
Date: 2016-9-6
Author: Claudia D. van Borkulo, with contributions from Sacha Epskamp and Alex Millner
Maintainer: Claudia van Borkulo <cvborkulo@gmail.com>
Imports: qgraph, IsingFit, IsingSampler, reshape2, stats, graphics,
utils
Description: This permutation based hypothesis test, suited for Gaussian and
binary data, assesses the difference between two networks based on several
invariance measures (network structure invariance, global strength invariance,
edge invariance). Network structures are estimated with l1-regularized partial
correlations (Gaussian data) or with l1-regularized logistic regression (eLasso,
binary data). Suited for comparison of independent and dependent samples
(currently, only for one group measured twice).
License: GPL-2
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2016-09-06 20:56:46 UTC; claudiavb
Repository: CRAN
Date/Publication: 2016-09-07 02:16:12