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| #' @keywords internal | ||
| "_PACKAGE" | ||
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| ## usethis namespace: start | ||
| #' ipd: Inference on Predicted Data | ||
| #' | ||
| #' The `ipd` package provides tools for statistical modeling and inference when | ||
| #' a significant portion of the outcome data is predicted by AI/ML algorithms. | ||
| #' It implements several state-of-the-art methods for inference on predicted | ||
| #' data (IPD), offering a user-friendly interface to facilitate their use in | ||
| #' real-world applications. | ||
| #' | ||
| #' This package is particularly useful in scenarios where predicted values | ||
| #' (e.g., from machine learning models) are used as proxies for unobserved | ||
| #' outcomes, which can introduce biases in estimation and inference. The `ipd` | ||
| #' package integrates methods designed to address these challenges. | ||
| #' | ||
| #' @section Features: | ||
| #' - Multiple IPD methods: `PostPI`, `PPI`, `PPI++`, and `PSPA` currently. | ||
| #' - Flexible wrapper functions for ease of use. | ||
| #' - Custom methods for model inspection and evaluation. | ||
| #' - Seamless integration with common data structures in R. | ||
| #' - Comprehensive documentation and examples. | ||
| #' | ||
| #' @section Key Functions: | ||
| #' - \code{\link{ipd}}: Main wrapper function which implements various methods for inference on predicted data for a specified model/outcome type (e.g., mean estimation, linear regression). | ||
| #' - \code{\link{simdat}}: Simulates data for demonstrating the use of the various IPD methods. | ||
| #' - \code{\link{print.ipd}}: Prints a brief summary of the IPD method/model combination. | ||
| #' - \code{\link{summary.ipd}}: Summarizes the results of fitted IPD models. | ||
| #' - \code{\link{tidy.ipd}}: Tidies the IPD method/model fit into a data frame. | ||
| #' - \code{\link{glance.ipd}}: Glances at the IPD method/model fit, returning a one-row summary. | ||
| #' - \code{\link{augment.ipd}}: Augments the data used for an IPD method/model fit with additional information about each observation. | ||
| #' | ||
| #' @section Documentation: | ||
| #' The package includes detailed documentation for each function, including | ||
| #' usage examples. A vignette is also provided to guide users through common | ||
| #' workflows and applications of the package. | ||
| #' | ||
| #' @section References: | ||
| #' For details on the statistical methods implemented in this package, please | ||
| #' refer to the associated manuscripts at the following references: | ||
| #' - \strong{PostPI}: Wang, S., McCormick, T. H., & Leek, J. T. (2020). Methods for correcting inference based on outcomes predicted by machine learning. Proceedings of the National Academy of Sciences, 117(48), 30266-30275. | ||
| #' - \strong{PPI}: Angelopoulos, A. N., Bates, S., Fannjiang, C., Jordan, M. I., & Zrnic, T. (2023). Prediction-powered inference. Science, 382(6671), 669-674. | ||
| #' - \strong{PPI++}: Angelopoulos, A. N., Duchi, J. C., & Zrnic, T. (2023). PPI++: Efficient prediction-powered inference. arXiv preprint arXiv:2311.01453. | ||
| #' - \strong{PSPA}: Miao, J., Miao, X., Wu, Y., Zhao, J., & Lu, Q. (2023). Assumption-lean and data-adaptive post-prediction inference. arXiv preprint arXiv:2311.14220. | ||
| #' | ||
| #' @name ipd-package | ||
| #' | ||
| #' @keywords package | ||
| #' | ||
| #' @examples | ||
| #' #-- Generate Example Data | ||
| #' | ||
| #' set.seed(12345) | ||
| #' | ||
| #' dat <- simdat(n = c(300, 300, 300), effect = 1, sigma_Y = 1) | ||
| #' | ||
| #' head(dat) | ||
| #' | ||
| #' formula <- Y - f ~ X1 | ||
| #' | ||
| #' #-- PostPI Analytic Correction (Wang et al., 2020) | ||
| #' | ||
| #' fit_postpi1 <- ipd(formula, method = "postpi_analytic", model = "ols", | ||
| #' | ||
| #' data = dat, label = "set") | ||
| #' | ||
| #' #-- PostPI Bootstrap Correction (Wang et al., 2020) | ||
| #' | ||
| #' nboot <- 200 | ||
| #' | ||
| #' fit_postpi2 <- ipd(formula, method = "postpi_boot", model = "ols", | ||
| #' | ||
| #' data = dat, label = "set", nboot = nboot) | ||
| #' | ||
| #' #-- PPI (Angelopoulos et al., 2023) | ||
| #' | ||
| #' fit_ppi <- ipd(formula, method = "ppi", model = "ols", | ||
| #' | ||
| #' data = dat, label = "set") | ||
| #' | ||
| #' #-- PPI++ (Angelopoulos et al., 2023) | ||
| #' | ||
| #' fit_plusplus <- ipd(formula, method = "ppi_plusplus", model = "ols", | ||
| #' | ||
| #' data = dat, label = "set") | ||
| #' | ||
| #' #-- PSPA (Miao et al., 2023) | ||
| #' | ||
| #' fit_pspa <- ipd(formula, method = "pspa", model = "ols", | ||
| #' | ||
| #' data = dat, label = "set") | ||
| #' | ||
| #' #-- Print the Model | ||
| #' | ||
| #' print(fit_postpi1) | ||
| #' | ||
| #' #-- Summarize the Model | ||
| #' | ||
| #' summ_fit_postpi1 <- summary(fit_postpi1) | ||
| #' | ||
| #' #-- Print the Model Summary | ||
| #' | ||
| #' print(summ_fit_postpi1) | ||
| #' | ||
| #' #-- Tidy the Model Output | ||
| #' | ||
| #' tidy(fit_postpi1) | ||
| #' | ||
| #' #-- Get a One-Row Summary of the Model | ||
| #' | ||
| #' glance(fit_postpi1) | ||
| #' | ||
| #' #-- Augment the Original Data with Fitted Values and Residuals | ||
| #' | ||
| #' augmented_df <- augment(fit_postpi1) | ||
| #' | ||
| #' head(augmented_df) | ||
| ## usethis namespace: end | ||
| NULL |
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