The 'EconDataverse' is a universe of open-source packages to work 
    seamlessly with economic data. This package is designed to make it easy to 
    download selected datasets that are preprocessed by 'EconDataverse' packages 
    and publicly hosted on 'Hugging Face'. Learn more about the 'EconDataverse' 
    at <https://www.econdataverse.org>.

The 'EconDataverse' is a universe of open-source packages to work 
    seamlessly with economic data. This package is designed to make it easy to 
    install and load multiple 'EconDataverse' packages in a single step. Learn
    more about the 'EconDataverse' at <https://www.econdataverse.org>.

Tools for general properties including price, quantity, elasticity, convexity, marginal revenue and manifold of various economics demand systems including Linear, Translog, CES, LES and CREMR.

Provides methods for estimating parameter-dependent network centrality measures with linear-in-means models. Both non linear least squares and maximum likelihood estimators are implemented. The methods allow for both link and node heterogeneity in network effects, endogenous network formation and the presence of unconnected nodes. The routines also compare the explanatory power of parameter-dependent network centrality measures with those of standard measures of network centrality. Benefits and features of the 'econet' package are illustrated using data from Battaglini and Patacchini (2018) and Battaglini, Patacchini, and Leone Sciabolazza (2020). For additional details, see the vignette <doi:10.18637/jss.v102.i08>.

Randomly generate a wide range of interaction networks with
             specified size, average degree, modularity, and topological
             structure. Sample nodes and links from within simulated networks
             randomly, by degree, by module, or by abundance. Simulations
             and sampling routines are implemented in 'FORTRAN', providing
             efficient generation times even for large networks. Basic
             visualization methods also included. Algorithms implemented
             here are described in de Aguiar et al. (2017) <arXiv:1708.01242>.