R/REndo-package.R

In REndo: Fitting Linear Models with Endogenous Regressors using Latent Instrumental Variables

#' @title Fitting Linear Models with Endogenous Regressors using Latent Instrumental Variables
#' @description
#' Fits linear models with endogenous regressor using latent instrumental variable approaches.
#'
#' The methods included in the package are Lewbel's (1997) <doi:10.2307/2171884> higher moments approach as well as
#' Lewbel's (2012) <doi:10.1080/07350015.2012.643126> heteroskedasticity approach, Park and Gupta's (2012) <doi:10.1287/mksc.1120.0718> joint estimation method
#' that uses Gaussian copula and Kim and Frees's (2007) <doi:10.1007/s11336-007-9008-1> multilevel generalized
#' method of moment approach that deals with endogeneity in a multilevel setting.
#' These are statistical techniques to address the endogeneity problem where no external instrumental variables are needed.
#'
#' The main functions to estimate models are:
#' \describe{
#' \item{\code{latentIV()}}{the latent instrumental variables method of Ebbes et al. (2005)}
#' \item{\code{copulaCorrection()}}{copula correction method proposed by Paek and Gupta (2012)}
#' \item{\code{hetErrorsIV()}}{heteroskedastic errors approach proposed by Lewbel(2012)}
#' \item{\code{higherMomentsIV()}}{higher moments method proposed by Lewbel (1997)}
#' \item{\code{multilevelIV()}}{multilevel GMM method proposed by Kim and Frees (2007)}
#' }
#'
#' \strong{Differences between current (2.0.0) and previous version of REndo}
#'
#' Note that with version 2.0.0 sweeping changes were which greatly improve functionality but break backwards compatibility.
#' Various bugs were fixed, performance improved, handling of S3 objects and methods across the package was harmonized,
#' and a set of argument checks has been added. Starting with REndo 2.0, all functions support the use
#' of transformations such as I(x^2) or log(x) in the formulas.
#' Moreover, the call of most of the functions (except latentIV() and multilevelIV()) changed from the previous versions, making use
#' of the Formula package.
#'
#' Check the NEWS file or our \href{https://github.com/mmeierer/REndo}{github page} for the latest updates and for reporting issues.
#'
#' See our publication in the Journal of Statistical Software for more details: \doi{10.18637/jss.v107.i03}.
#'
#' @references Gui R, Meierer M, Schilter P, Algesheimer R (2023). “REndo: Internal Instrumental Variables to Address Endogeneity.” Journal of Statistical Software, 107 (3), 1-43. \doi{10.18637/jss.v107.i03}
#'
#' @aliases REndo-package
#' @name REndo
#'
#' @useDynLib REndo, .registration=TRUE
#' @importFrom Rcpp sourceCpp
#'
"_PACKAGE"

#@import RcppEigen