Available on CRAN
- SphericalCubature - Numerical integration of functions over spheres and balls
- SimplicialCubature - Numerical integration of functions over simplices
- mvmesh - Multivariate meshes
- gensphere - Generalized spherical distributions
- ecdfHT - Empirical cdf for Heavy Tailed data
An interactive online application to demonstrate methods in the paper “Extreme Value Analysis Without the Largest Values: What Can Be Done? (Zou, Samorodnitsky and Davis, 2017). Users can either choose from existing real data examples (earthquake fatalities, Google+) or upload their own data. Users can artificially remove a number of extreme values from the data and compare estimation results before and after the removal. This application allows real-time computation based on user inputs and visualizations of results with interactive plots.
Simulation of preferential attachment networks
This is an implementation of the generalized scale-free network model described in Section 5 of “Fitting the linear preferential attachment model (Wan et al.) https://arxiv.org/abs/1703.03095
The two simulation programs, netSim1 and netSim2, correspond to different starting values. netSim1 starts with one node 0, with a self loop, 0 -> 0. netSim2 starts with two nodes 0 and 1, and a connecting edge 0 -> 1. The computation complexity is O(n).
Parameter estimation in a preferential attachment model:
This is an implementation of the parameter estimation methods for linear preferential attachment model described in Fitting the linear preferential attachment model in https://arxiv.org/abs/1703.03095.
The R-scripts netfnsMLE.R and netfnsSnap.R correspond to estimation algorithms for the full network (MLE) and a snapshot of the network, where the details can be found in Section 3 and 4 of Wan et al. respectively.
UMass group (Towsley, Gong, Atwood)
- Software for node and graph classification developed using neural networks https://github.com/jcatw/dcnn
- Software for generating networks with power law degree distributions (see also P. Wan) https://github.com/jcatw/quicknet
R-functions for univariate and multivariate heavy tail analysis
Compendium of R-functions used in both univariate and multivariate heavy tail analysis. Some are outlined in the book Heavy-Tail Phenomena: Probabilistic and Statistical Modeling. Springer-Verlag, New York, in the appendix starting on p. 364. Use at your own risk.
Software for graph feature discovery.