Network-Valued Data Analysis
Fréchet Mean of Network-Valued Data
nevada: Network-Valued Data Analysis
MDS Visualization of Network Distributions
Network-Valued Data Constructor
Pipe operator
Coercion to Network-Valued Data Object
Coercion to Vertex Partition
Pairwise Distance Matrix Between Two Samples of Networks
Distances Between Networks
Transform distance matrix in edge properties of minimal spanning tree
Sigma-Algebra generated by a Partition
Inner-Products Between Networks
Power Simulations for Permutation Tests
Network-Valued to Matrix-Valued Data
Network Representation Functions
Two-Sample Stochastic Block Model Generator
Graph samplers using edge distributions
Test Statistics for Network Populations
Full, intra and inter subgraph generators
Global Two-Sample Test for Network-Valued Data
Local Two-Sample Test for Network-Valued Data
Fréchet Variance of Network-Valued Data Around a Given Network
Fréchet Variance of Network-Valued Data from Inter-Point Distances
A flexible statistical framework for network-valued data analysis. It leverages the complexity of the space of distributions on graphs by using the permutation framework for inference as implemented in the 'flipr' package. Currently, only the two-sample testing problem is covered and generalization to k samples and regression will be added in the future as well. It is a 4-step procedure where the user chooses a suitable representation of the networks, a suitable metric to embed the representation into a metric space, one or more test statistics to target specific aspects of the distributions to be compared and a formula to compute the permutation p-value. Two types of inference are provided: a global test answering whether there is a difference between the distributions that generated the two samples and a local test for localizing differences on the network structure. The latter is assumed to be shared by all networks of both samples. References: Lovato, I., Pini, A., Stamm, A., Vantini, S. (2020) "Model-free two-sample test for network-valued data" <doi:10.1016/j.csda.2019.106896>; Lovato, I., Pini, A., Stamm, A., Taquet, M., Vantini, S. (2021) "Multiscale null hypothesis testing for network-valued data: Analysis of brain networks of patients with autism" <doi:10.1111/rssc.12463>.
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