The CDatanet package simulates and estimates peer effect models and network formation models. The peer effect models include linear-in-means models (Lee, 2004; Lee et al., 2010),
Tobit models (Xu and Lee, 2015), and discrete numerical data models (Houndetoungan, 2024).
The network formation models include pairwise regressions with degree heterogeneity (Graham, 2017; Yan et al., 2019) and exponential random graph models (Mele, 2017).
To enhance computation speed, CDatanet uses C++ via the Rcpp package (Eddelbuettel et al., 2011).
Maintainer: Aristide Houndetoungan ahoundetoungan@gmail.com
Eddelbuettel, D., & Francois, R. (2011). Rcpp: Seamless R and C++ integration. Journal of Statistical Software, 40(8), 1-18, tools:::Rd_expr_doi("10.18637/jss.v040.i08").
Houndetoungan, E. A. (2024). Count Data Models with Heterogeneous Peer Effects. Available at SSRN 3721250, tools:::Rd_expr_doi("10.2139/ssrn.3721250").
Lee, L. F. (2004). Asymptotic distributions of quasi-maximum likelihood estimators for spatial autoregressive models. Econometrica, 72(6), 1899-1925, tools:::Rd_expr_doi("10.1111/j.1468-0262.2004.00558.x").
Lee, L. F., Liu, X., & Lin, X. (2010). Specification and estimation of social interaction models with network structures. The Econometrics Journal, 13(2), 145-176, tools:::Rd_expr_doi("10.1111/j.1368-423X.2010.00310.x")
Xu, X., & Lee, L. F. (2015). Maximum likelihood estimation of a spatial autoregressive Tobit model. Journal of Econometrics, 188(1), 264-280, tools:::Rd_expr_doi("10.1016/j.jeconom.2015.05.004").
Graham, B. S. (2017). An econometric model of network formation with degree heterogeneity. Econometrica, 85(4), 1033-1063, tools:::Rd_expr_doi("10.3982/ECTA12679").
Mele, A. (2017). A structural model of dense network formation. Econometrica, 85(3), 825-850, tools:::Rd_expr_doi("10.3982/ECTA10400").
Yan, T., Jiang, B., Fienberg, S. E., & Leng, C. (2019). Statistical inference in a directed network model with covariates. Journal of the American Statistical Association, 114(526), 857-868, tools:::Rd_expr_doi("10.1080/01621459.2018.1448829").
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