hdpGLM_package: hdpGLM: A package for computating Hierarchical Dirichlet...

hdpGLM_packageR Documentation

hdpGLM: A package for computating Hierarchical Dirichlet Process Generalized Linear Models

Description

Further information is available at: http://www.diogoferrari.com/hdpGLM/index.html

References:

- Ferrari, D. (2020). Modeling Context-Dependent Latent Effect Heterogeneity. Political Analysis, 28(1), 20–46.

- Mukhopadhyay, S., & Gelfand, A. E. (1997). Dirichlet Process Mixed Generali- zed Linear Models. Journal of the American Statistical Association, 92(438), 633–639.

- Hannah, L. A., Blei, D. M., & Powell, W. B. (2011). Dirichlet Process Mix- tures of Generalized Linear Models. Journal of Machine Learning Research, 12(Jun), 1923–1953.

- Heckman, J. J., & Vytlacil, E. J. (2007). Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation. Handbook of Econometrics, 6(), 4779–4874.

Details

The package implements a hierarchical Dirichlet process Generalized Linear Model as proposed in Ferrari (2020) Modeling Context-Dependent Latent Effect Heterogeneity, which expands the non-parametric Bayesian models proposed in Mukhopadhyay and Gelfand (1997), Hannah (2011), and Heckman and Vytlacil (2007) to deal with context-dependent cases. The package can be used to estimate latent heterogeneity in the marginal effect of GLM linear coeffi- cients, to cluster data points based on that latent heterogeneity, and to investigate the occurrence of Simpson’s Paradox due to latent or omitted fea- tures.


hdpGLM documentation built on May 7, 2022, 1:06 a.m.