Nothing
#' 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
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.