% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nonprobsampling-package.R
\docType{package}
\name{nonprobsampling-package}
\alias{nonprobsampling}
\alias{nonprobsampling-package}
\title{nonprobsampling: Inference for Nonprobability Samples Using Multiple Reference Surveys}
\description{
\pkg{nonprobsampling} implements pseudo-weighting methods for
finite population inference from nonprobability samples, such as
convenience samples, volunteer cohorts, and opt-in panels. Because the
participation mechanism in a nonprobability sample is unknown, unadjusted
estimates of population means and prevalences may be biased. The package
addresses this issue by leveraging auxiliary information from one or more
probability reference surveys to estimate participation probabilities and
using their inverses as pseudo-weights to obtain bias-corrected estimates
of finite population means and prevalences.
}
\details{
The package implements the generalized estimating equation framework of
Landsman et al. (2026). Pseudo-weights are obtained by solving estimating
equations that equate auxiliary variable totals estimated from the
nonprobability sample with the corresponding totals estimated from one or
more probability reference surveys. Totals from the nonprobability sample are
computed using estimated pseudo-weights, whereas totals from the reference
surveys are computed using the known survey sampling weights.

Several one reference methods arise as special cases of this framework under
different choices of the weight and estimating functions:
\itemize{
  \item the raking ratio calibration method of
    Landsman et al. (2026);
  \item the adjusted logistic propensity (ALP) method of
    Wang, Valliant, and Li (2021);
  \item the Chen--Li--Wu (CLW) method of Chen, Li, and Wu (2020).
}

A key feature of the package is the multi-reference extension of the
calibration method. This extension enables the integration of auxiliary
information across multiple surveys when no single reference survey
contains all variables relevant to participation (Landsman et al., 2026),
with an optional cumulative precalibration step to preserve information on
the relationships between overlapping and unique auxiliary variables across
reference surveys.

Variance estimation is based on Taylor linearization. The resulting analytic
variance estimator accounts for uncertainty from pseudo-weight estimation and
for the sampling designs of the reference surveys through integration with
the \pkg{survey} package. The package also supports bootstrap-based variance
estimation when bootstrap weights are provided with the reference surveys.
}
\section{Typical workflow}{

Estimation is carried out in two steps:
\enumerate{
  \item \code{\link{est_pw}()} estimates pseudo-weights using the
    nonprobability sample and one or more reference survey design objects.
    This step fits the participation model and stores the internal quantities
    needed for subsequent estimation. It does not require an outcome variable.
  \item \code{\link{pwmean}()} uses the object returned by \code{est_pw()}
    to estimate a pseudo-weighted mean or prevalence for an outcome, either
    overall or by category. It also returns the standard error and confidence
    interval.
}

Numerical settings for solving the estimating equations can be specified with
\code{\link{pw_solver_control}()}.
}

\section{Datasets}{

The package includes example datasets for demonstrating one reference and
multiple reference analyses: \code{\link{sc}} is a nonprobability sample,
\code{\link{sp1}} and \code{\link{sp2}} are probability reference surveys,
and \code{\link{sp1_bootstrap}} contains replicate weights for
\code{sp1}. See the package vignette for worked examples.
}

\references{
Chen, Y., Li, P., and Wu, C. (2020).
Doubly robust inference with nonprobability survey samples.
\emph{Journal of the American Statistical Association},
115(532), 2011--2021.
doi:10.1080/01621459.2019.1677241

Landsman, V., Wang, L., Carrillo-Garcia, I., Mitani, A. A.,
Smith, P. M., Graubard, B. I., Bui, T., and Carnide, N. (2026).
Correction for participation bias in nonprobability samples using multiple
reference surveys.
\emph{Statistics in Medicine}, 45(3--5).
doi:10.1002/sim.70403

Wang, L., Valliant, R., and Li, Y. (2021).
Adjusted logistic propensity weighting methods for population inference
using nonprobability volunteer-based epidemiologic cohorts.
\emph{Statistics in Medicine}, 40(24), 5237--5250.
doi:10.1002/sim.9122
}
\seealso{
\code{\link{est_pw}}, \code{\link{pwmean}}, \code{\link{pw_solver_control}}
}
\author{
\strong{Maintainer}: Jiakun Lin \email{jiak.lin@alumni.utoronto.ca}

Authors:
\itemize{
  \item Victoria Landsman
  \item Aya A. Mitani
}

}
\keyword{internal}
