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MuxVizPy
Public.github
Publicstructural_robustness
PublicA modular Python library to study the structural robustness of complex networks. It computes spectral-entropy metrics, entanglement-based node importance, and simulates centrality-driven dismantling while tracking the largest connected component. Includes correlation analysis, examples, and pytest tests.bioRC
PublicStochasticDynamics
PublicStochasticDynamics is an R package for simulating and visualizing stochastic processes on networks, including epidemic (SIR, SEIR, SIRS), opinion, and reaction–diffusion models. Provides incidence analysis, interactive 3D visualization with rgl, and video export tools.collectivedyn
PublicR package for simulating and analyzing collective dynamics of coupled systems on networks. Includes continuous-time ODEs (Lorenz, Rössler, Chua, Duffing, etc.), discrete maps (Logistic, Hénon, ARMA, Harmonic), Kuramoto and double-well models, with adaptive rewiring and visualization tools.RobustnessProfiles
PublicSimulation and analysis of network robustness under node removal strategies. Includes standard centrality-based attacks and entanglement-based robustness using mutual information profiles. Provides tools to generate synthetic networks, compute robustness curves, and visualize critical thresholds.LLM-Agents
PublicSpectralEntropyLib
PublicR package providing information-theoretic tools for complex networks via spectral entropies and divergences. Implements von Neumann and Rényi entropies, Jensen–Shannon and Kullback–Leibler divergences, spectral gap and density-matrix based measures, enabling network comparison, profiling, and inference.SpectralGeometryLib
PublicSpectralGeometry is an R package for spectral analysis of complex networks. It computes Laplacians (combinatorial, random-walk, symmetric), eigen-spectra, heat kernels, diffusion distances, and low-dimensional embeddings, plus metrics for partitions and multilayer graphs.MultiNetLib
PublicNetGrowLib
PublicNetGrowLib is an R package providing a collection of network growth models and tools for analyzing their degree distributions. It implements classical models (Barabási–Albert, CHKNS, age-biased “Darknet”) as well as exploratory clustered variants, with utilities for log/linear binning, empirical degree distributions, and visualization.seir_deniers
Publicperturbnet
Publicinfodemap
Publicfunctional_robustness
Publicforecasting
PublicA modular Python library for time series forecasting with SARIMAX, symbolic entropy rate estimation via Lempel-Ziv methods, and geospatial analysis using the haversine distance. Includes support for endogenous weekly patterns, exogenous drivers, and flexible data preprocessing.embedding-dim
Publicndm
Publicjacobian_geometry
PublicFERM_python
Publicoad
PublicPierre-Auger-Multiplex
PublicXenopus-Multiplex
Public