Generates random and quasi-random space-filling sequences. Supports the following sequences: 'Halton', 'Sobol', 'Owen'-scrambled 'Sobol',  'Owen'-scrambled 'Sobol' with errors distributed as blue noise, progressive jittered, progressive multi-jittered ('PMJ'), 'PMJ' with blue noise, 'PMJ02', and 'PMJ02' with blue noise. Includes a 'C++' 'API'. Methods derived from "Constructing Sobol sequences with better two-dimensional projections" (2012) <doi:10.1137/070709359> S. Joe and F. Y. Kuo, "Progressive Multi-Jittered Sample Sequences" (2018) <https://graphics.pixar.com/library/ProgressiveMultiJitteredSampling/paper.pdf> Christensen, P., Kensler, A. and Kilpatrick, C., and "A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space" (2019) E. Heitz, B. Laurent, O. Victor, C. David and I. Jean-Claude, <doi:10.1145/3306307.3328191>.

Social network analysis is becoming commonplace in many social 
    science disciplines, but access to useful network data, especially 
    among marginalized populations, still remains a formidable challenge. 
    This package mitigates that problem by providing tools to simulate spatial 
    Bernoulli networks as proposed in Carter T. Butts 
    (2002, ISBN:978-0-493-72676-2), "Spatial models of large-scale interpersonal            
    networks." Using this package, network analysts can simulate a spatial point 
    process or sequence with a given number of nodes inside a geographical 
    boundary and estimate the probability of a tie formation between all node 
    pairs. When simulating a network, an analyst can choose between five spatial
    interaction functions. The package also enables quick comparison of summary             
    statistics for simulated networks and provides simple to use plotting 
    methods for its classes that return plots which can be further refined with 
    the 'ggplot2' package.

Latent space models for multivariate networks (multiplex) estimated via MCMC algorithm. See D Angelo et al. (2018) <arXiv:1803.07166> and D Angelo et al. (2018) <arXiv:1807.03874>.