A New Method of Identifying Core Designers and Teams Based on the Importance and Similarity of Networks.

In the process of product collaborative design, the association between designers can be described by a complex network. Exploring the importance of the nodes and the rules of information dissemination in such networks is of great significance for distinguishing its core designers and potential designer teams, as well as for accurate recommendations of collaborative design tasks. Based on the neighborhood similarity model, combined with the idea of network information propagation, and with the help of the ReLU function, this paper proposes a new method for judging the importance of nodes-LLSR.

EBglmnet: a comprehensive R package for sparse generalized linear regression models.

Summary: EBglmnet is an R package implementing empirical Bayesian method with both lasso (EBlasso) and elastic net (EBEN) priors for generalized linear models. In our previous studies, both EBlasso and EBEN outperformed other state-of-the-art methods such as lasso and elastic net in inferring sparse genotype and phenotype associations, in which the number of covariates is typically much larger than the sample size. While high density genetic markers can be easily obtained nowadays in genetics and population analysis thanks to the advancements in molecular high throughput technologies, EBglmnet will be a very useful tool for statistical modeling in this area.