From ee4b5bc733597e3c2ce49c0e6f77452606b3f995 Mon Sep 17 00:00:00 2001 From: Awan Afiaz <87647778+awanafiaz@users.noreply.github.com> Date: Mon, 30 Sep 2024 14:06:57 -0700 Subject: [PATCH] update readme after namechange --- README.Rmd | 6 +++--- README.md | 54 +++++++++++++++++++++++++++--------------------------- 2 files changed, 30 insertions(+), 30 deletions(-) diff --git a/README.Rmd b/README.Rmd index a1d1b22..eb00e88 100644 --- a/README.Rmd +++ b/README.Rmd @@ -28,9 +28,9 @@ knitr::opts_chunk$set(collapse = TRUE, comment = "#>", Using predictions from pre-trained algorithms as outcomes in downstream statistical analyses can lead to biased estimates and misleading conclusions. The statistical challenges encountered when drawing inference on predicted data (IPD) include: -1. Understanding the relationship between predicted outcomes and their true, unobserved counterparts -2. Quantifying the robustness of the AI/ML models to resampling or uncertainty about the training data -3. Appropriately propagating both bias and uncertainty from predictions into downstream inferential tasks +1. Understanding the relationship between predicted outcomes and their true, unobserved counterparts. +2. Quantifying the robustness of the AI/ML models to resampling or uncertainty about the training data. +3. Appropriately propagating both bias and uncertainty from predictions into downstream inferential tasks. Several works have proposed methods for IPD, including post-prediction inference (PostPI) by [Wang et al., 2020](https://www.pnas.org/doi/suppl/10.1073/pnas.2001238117), prediction-powered inference (PPI) and PPI++ by [Angelopoulos et al., 2023a](https://www.science.org/doi/10.1126/science.adi6000) and [Angelopoulos et al., 2023b](https://arxiv.org/abs/2311.01453), and post-prediction adaptive inference (PSPA) by [Miao et al., 2023](https://arxiv.org/abs/2311.14220). Each method was developed to perform inference on a quantity such as the outcome mean or quantile, or a regression coefficient, when we have: diff --git a/README.md b/README.md index 187a7ec..7d50357 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ -[![R-CMD-check](https://github.com/awanafiaz/IPD/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/awanafiaz/IPD/actions/workflows/R-CMD-check.yaml) +[![R-CMD-check](https://github.com/awanafiaz/ipd/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/awanafiaz/ipd/actions/workflows/R-CMD-check.yaml) @@ -30,11 +30,11 @@ conclusions. The statistical challenges encountered when drawing inference on predicted data (IPD) include: 1. Understanding the relationship between predicted outcomes and their - true, unobserved counterparts + true, unobserved counterparts. 2. Quantifying the robustness of the AI/ML models to resampling or - uncertainty about the training data + uncertainty about the training data. 3. Appropriately propagating both bias and uncertainty from predictions - into downstream inferential tasks + into downstream inferential tasks. Several works have proposed methods for IPD, including post-prediction inference (PostPI) by [Wang et al., @@ -75,7 +75,7 @@ package. ## Installation To install the development version of `ipd` from -[GitHub](https://github.com/awanafiaz/IPD), you can use the `devtools` +[GitHub](https://github.com/awanafiaz/ipd), you can use the `devtools` package: ``` r @@ -83,9 +83,9 @@ package: install.packages("devtools") -#-- Install the IPD package from GitHub +#-- Install the ipd package from GitHub -devtools::install_github("awanafiaz/IPD") +devtools::install_github("awanafiaz/ipd") ``` ## Usage @@ -106,9 +106,9 @@ observations used to fit a prediction model, and “labeled” and the simulated features of interest. ``` r -#-- Load the IPD Library +#-- Load the ipd Library -library(IPD) +library(ipd) #-- Generate Example Data for Linear Regression @@ -252,7 +252,7 @@ formula <- Y - f ~ X1 nboot <- 200 -IPD::ipd(formula, +ipd::ipd(formula, method = "postpi_boot", model = "ols", data = dat, label = "set", @@ -278,7 +278,7 @@ IPD::ipd(formula, ``` r #-- Fit the PostPI Analytic Correction -IPD::ipd(formula, +ipd::ipd(formula, method = "postpi_analytic", model = "ols", data = dat, label = "set") |> @@ -302,7 +302,7 @@ IPD::ipd(formula, ``` r #-- Fit the PPI Correction -IPD::ipd(formula, +ipd::ipd(formula, method = "ppi", model = "ols", data = dat, label = "set") |> @@ -326,7 +326,7 @@ IPD::ipd(formula, ``` r #-- Fit the PPI++ Correction -IPD::ipd(formula, +ipd::ipd(formula, method = "ppi_plusplus", model = "ols", data = dat, label = "set") |> @@ -350,7 +350,7 @@ IPD::ipd(formula, ``` r #-- Fit the PSPA Correction -IPD::ipd(formula, +ipd::ipd(formula, method = "pspa", model = "ols", data = dat, label = "set") |> @@ -379,7 +379,7 @@ and `augment` methods to facilitate easy model inspection: nboot <- 200 -fit_postpi <- IPD::ipd(formula, +fit_postpi <- ipd::ipd(formula, method = "postpi_boot", model = "ols", data = dat, label = "set", @@ -460,20 +460,20 @@ developers at [ssalerno@fredhutch.org](ssalerno@fredhutch.org). ## Contributing Contributions are welcome! Please open an issue or submit a pull request -on [GitHub](https://github.com/awanafiaz/IPD). The following +on [GitHub](https://github.com/awanafiaz/ipd). The following method/model combinations are currently implemented: -| Method | Mean Estimation | Quantile Estimation | Linear Regression | Logistic Regression | Poisson Regression | Multiclass Regression | -|-----------------------------------------------------------------|--------------------|---------------------|--------------------|---------------------|--------------------|-----------------------| -| [PostPI](https://www.pnas.org/doi/full/10.1073/pnas.2001238117) | :x: | :x: | :white_check_mark: | :white_check_mark: | :x: | :x: | -| [PPI](https://www.science.org/doi/10.1126/science.adi6000) | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: | :x: | -| [PPI++](https://arxiv.org/abs/2311.01453) | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: | :x: | -| [PSPA](https://arxiv.org/abs/2311.14220) | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: | -| [PSPS](https://arxiv.org/abs/2405.20039) | :x: | :x: | :x: | :x: | :x: | :x: | -| [PDC](https://arxiv.org/abs/2312.06478) | :x: | :x: | :x: | :x: | :x: | :x: | -| [Cross-PPI](https://www.pnas.org/doi/10.1073/pnas.2322083121) | :x: | :x: | :x: | :x: | :x: | :x: | -| [PPBoot](https://arxiv.org/abs/2405.18379) | :x: | :x: | :x: | :x: | :x: | :x: | -| [DSL](https://arxiv.org/abs/2306.04746) | :x: | :x: | :x: | :x: | :x: | :x: | +| Method | Mean Estimation | Quantile Estimation | Linear Regression | Logistic Regression | Poisson Regression | Multiclass Regression | +|----|----|----|----|----|----|----| +| [PostPI](https://www.pnas.org/doi/full/10.1073/pnas.2001238117) | :x: | :x: | :white_check_mark: | :white_check_mark: | :x: | :x: | +| [PPI](https://www.science.org/doi/10.1126/science.adi6000) | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: | :x: | +| [PPI++](https://arxiv.org/abs/2311.01453) | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: | :x: | +| [PSPA](https://arxiv.org/abs/2311.14220) | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: | +| [PSPS](https://arxiv.org/abs/2405.20039) | :x: | :x: | :x: | :x: | :x: | :x: | +| [PDC](https://arxiv.org/abs/2312.06478) | :x: | :x: | :x: | :x: | :x: | :x: | +| [Cross-PPI](https://www.pnas.org/doi/10.1073/pnas.2322083121) | :x: | :x: | :x: | :x: | :x: | :x: | +| [PPBoot](https://arxiv.org/abs/2405.18379) | :x: | :x: | :x: | :x: | :x: | :x: | +| [DSL](https://arxiv.org/abs/2306.04746) | :x: | :x: | :x: | :x: | :x: | :x: | ## License