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The ergm package supports the statistical analysis and simulation of network data. It anchors the statnet suite of packages for network analysis in R introduced in a special issue in Journal of Statistical Software in 2008. This article provides an overview of the functionality and performance improvements in the 2021 ergm 4.0 release. These include more flexible handling of nodal covariates, operator terms that extend and simplify model specification, new models for networks with valued edges, improved handling of constraints on the sample space of networks, performance enhancements to the Markov chain Monte Carlo and maximum likelihood estimation algorithms, broader and faster searching for networks with certain target statistics using simulated annealing, and estimation with missing edge data. We also identify the new packages in the statnet suite that extend ergms functionality to other network data types and structural features, and the robust set of online resources that support the statnet development process and applications.
Theano is a linear algebra compiler that optimizes a users symbolically-specified mathematical computations to produce efficient low-level implementations. In this paper, we present new features and efficiency improvements to Theano, and benchmarks d
This paper describes the recent development of ESPnet (https://github.com/espnet/espnet), an end-to-end speech processing toolkit. This project was initiated in December 2017 to mainly deal with end-to-end speech recognition experiments based on sequ
Many modern datasets dont fit neatly into $n times p$ matrices, but most techniques for measuring statistical stability expect rectangular data. We study methods for stability assessment on non-rectangular data, using statistical learning algorithms
We investigate a Poisson sampling design in the presence of unknown selection probabilities when applied to a population of unknown size for multiple sampling occasions. The fixed-population model is adopted and extended upon for inference. The compl
In this paper, we develop a new estimation procedure based on the non-linear conjugate gradient (NCG) algorithm for the Box-Cox transformation cure rate model. We compare the performance of the NCG algorithm with the well-known expectation maximizati