R/FPScausal-package.R

In FPScausal: Functional Propensity Score for Causal Inference

#' FPScausal: Causal Inference with Functional Propensity Score Weighting
#'
#' @description
#' FPScausal implements the functional propensity score (FPS) weighting
#' methodology for causal inference with functional treatments.  Given a
#' functional treatment X(t), a set of scalar and/or functional confounders,
#' and an outcome (continuous, binary, or functional), the package:
#'
#' 1. Decomposes X(t) into Functional Principal Component (FPC) scores via
#'    \pkg{fda}.
#' 2. Estimates covariate-balancing weights by maximising the empirical
#'    likelihood subject to balancing constraints and solving the resulting
#'    dual problem via the BFGS quasi-Newton algorithm.
#' 3. Estimates the causal effect function \eqn{\mu(t)} (scalar/binary
#'    outcome) or causal effect surface \eqn{\mu(s,t)} (functional outcome)
#'    via weighted least squares.
#' 4. Provides pointwise bootstrap confidence intervals and a rich set of
#'    diagnostic and result plots.
#'
#' @section Main functions:
#' \describe{
#'   \item{\code{\link{fps_weighting}}}{Estimate FPS weights.}
#'   \item{\code{\link{fps_effect_estimation}}}{Estimate causal effects using
#'     FPS weights.}
#'   \item{\code{\link{simulate_fps_data}}}{Generate synthetic datasets for
#'     testing and vignette illustrations.}
#' }
#'
#' @section S3 methods:
#' Both \code{fps_weighting} and \code{fps_effect_estimation} objects support
#' \code{print}, \code{summary}, and \code{plot} with a \code{type} argument.
#'
#' @section References:
#' Ciardulli, S. and Fontana, N. (2026).
#' Functional propensity score weighting for causal inference with functional 
#' treatments, covariates, and outcomes. \emph{Preprint}.
#'
#' @keywords internal
"_PACKAGE"