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.

grid, Rmpfr, abind, mvtnorm, tidyr, igraph, qgraph, network,
stats, MASS, methods, RColorBrewer, reshape, ggrepel,
gridExtra, rlang, GGally, ggvis
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.

Statistical Comparison of Two Networks Based on Three Invariance
Measures
Claudia D. van Borkulo, with contributions from Sacha Epskamp and Alex Millner
qgraph, IsingFit, IsingSampler, reshape2, stats, graphics,
utils
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).

c(
person("Claudia", "van Borkulo", email = "cvborkulo@gmail.com",role = c("aut","cre")),
person("Sacha", "Epskamp", role = c("aut")),
person("Payton", "Jones", role = c("aut")),
person("Jonas", "Haslbeck", role = c("ctb")),
person("Alex", "Millner", role = c("ctb")))
qgraph, IsingFit, IsingSampler, reshape2, stats, graphics,
utils, Matrix, methods
This permutation based hypothesis test, suited for Gaussian and binary data,
assesses the difference between two networks based on several invariance measures
(e.g., 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. For dependent samples, only supported for data of
one group which is measured twice. See van Borkulo et al. (2017)
<doi:10.13140/RG.2.2.29455.38569>.
Claudia van Borkulo [aut, cre],
Sacha Epskamp [aut],
Payton Jones [aut],
Jonas Haslbeck [ctb],
Alex Millner [ctb]