R/lspartition-package.R

In lspartition: Nonparametric Estimation and Inference Procedures using Partitioning-Based Least Squares Regression

#'Nonparametric Estimation and Inference using Partitioning-Based Least Squares Regression
#'@description This package provides tools for statistical analysis using B-splines, wavelets, and
#'             piecewise polynomials as described in
#'             \href{https://arxiv.org/abs/1804.04916}{Cattaneo, Farrell and Feng (2019a)}:
#'             \code{\link{lsprobust}} for least squares point estimation with robust bias-corrected pointwise and
#'             uniform inference procedures; \code{\link{lspkselect}} for data-driven procedures
#'             for selecting the IMSE-optimal number of partitioning knots; \code{\link{lsprobust.plot}}
#'             for regression plots with robust confidence intervals and confidence bands;
#'             \code{\link{lsplincom}} for estimation and inference for linear combination of regression
#'             functions of different groups.
#'
#'             The companion software article, \href{https://arxiv.org/abs/1906.00202}{Cattaneo, Farrell and Feng (2019b)},
#'             provides further implementation details and empirical illustrations.
#'
#'@importFrom stats lm sd complete.cases quantile qnorm poly rbinom rnorm dnorm integrate
#'@importFrom matrixStats rowProds colProds colMaxs colMins rowMins rowMaxs
#'@importFrom combinat xsimplex
#'@importFrom splines splineDesign
#'@importFrom pracma bernoulli
#'@importFrom mgcv tensor.prod.model.matrix
#@importFrom graphics lines plot polygon
#'@importFrom MASS ginv
#@importFrom dplyr distinct
#'@import ggplot2
#'@docType package
#'@name lspartition-package
#'@author Matias D. Cattaneo, Princeton University, Princeton, NJ. \email{cattaneo@princeton.edu}.
#'
#'        Max H. Farrell, University of Chicago, Chicago, IL. \email{max.farrell@chicagobooth.edu}.
#'
#'        Yingjie Feng (maintainer), Princeton University, Princeton, NJ. \email{yingjief@princeton.edu}.
#'
#'@references
#' Cattaneo, M. D., M. H. Farrell, and Y. Feng (2019a): \href{https://arxiv.org/abs/1804.04916}{Large Sample Properties of Partitioning-Based Series Estimators}. Annals of Statistics, forthcoming. arXiv:1804.04916.
#'
#' Cattaneo, M. D., M. H. Farrell, and Y. Feng (2019b): \href{https://arxiv.org/abs/1906.00202}{lspartition: Partitioning-Based Least Squares Regression}. R Journal, forthcoming. arXiv:1906.00202.
#'
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