R Package

Native R package for semisupervised mean inference with AIPW and calibration.

ppiAIPW takes labeled outcomes, labeled predictions, and unlabeled predictions, and returns point estimates, standard errors, confidence intervals, calibration diagnostics, and a Wald causal wrapper.

R Quickstart Open Package Source

Install

Install the R package from GitHub or from a local checkout

The R package lives in the repository subdirectory r/ppiAIPW, so GitHub installs should target that subdirectory explicitly.

GitHub install with remotes

remotes::install_github(
  "Larsvanderlaan/ppi-aipw",
  subdir = "r/ppiAIPW"
)

GitHub install with pak

pak::pak("Larsvanderlaan/ppi-aipw/r/ppiAIPW")

Local source install

R CMD INSTALL r/ppiAIPW

Quickstart

Start with mean_inference(...)

Use mean_inference(...) when you want the estimate, standard error, confidence interval, fitted calibrator, and diagnostics in one call. 

library(ppiAIPW)

result = mean_inference(
    Y,
    Yhat,
    Yhat_unlabeled,
    method = "monotone_spline",
    alpha = 0.1
)

estimate = result$pointestimate
standard_error = result$se
lower = result$ci[[1]]
upper = result$ci[[2]]

summary(result)

mean_pointestimate()

Return only the point estimate when you do not need the full result object.

mean_se() and mean_ci()

Pull out uncertainty summaries directly for scripting or reporting pipelines.

method="auto"

Candidate methods include "aipw", "linear", "monotone_spline", and "isotonic".

calibration_diagnostics()

Optional honest out-of-fold calibration check from a result or fitted model.

causal_inference()

Estimate arm means and control-vs-treatment ATEs from predicted potential outcomes.

compute_two_sample_balancing_weights()

Construct nonnegative labeled-sample balancing weights.

Vignettes

Three starting points for R users

These documents live in the package source and install with the package, so they can be opened locally after installation.

Notes

R interface, same core workflow

The R package keeps the same core workflow as the Python package, with an R-native interface.

Core API

mean_inference(), mean_pointestimate(), mean_se(), mean_ci(), diagnostics, weights, and the causal wrapper are all available in R.

Result objects

Functions return S3 objects with print(), summary(), and plot() methods.

Install path

The package lives in r/ppiAIPW, so GitHub installs should use the repository subdirectory.