R/hdpGLM_package.R

In hdpGLM: Hierarchical Dirichlet Process Generalized Linear Models

#' hdpGLM: A package for computating Hierarchical Dirichlet Process Generalized
#' Linear Models
#'
#'
#' 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. 
#'
#' @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.
#'
#'
#' @docType package
#' @name hdpGLM_package
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