Package: ipd 0.1.2

Stephen Salerno

ipd: Inference on Predicted Data

Performs valid statistical inference on predicted data (IPD) using recent methods, where for a subset of the data, the outcomes have been predicted by an algorithm. Provides a wrapper function with specified defaults for the type of model and method to be used for estimation and inference. Further provides methods for tidying and summarizing results. Salerno et al., (2024) <doi:10.48550/arXiv.2410.09665>.

Authors:Stephen Salerno [aut, cre, cph], Jiacheng Miao [aut], Awan Afiaz [aut], Kentaro Hoffman [aut], Anna Neufeld [aut], Qiongshi Lu [aut], Tyler H McCormick [aut], Jeffrey T Leek [aut]

ipd_0.1.2.tar.gz
ipd_0.1.2.zip(r-4.5)ipd_0.1.2.zip(r-4.4)ipd_0.1.2.zip(r-4.3)
ipd_0.1.2.tgz(r-4.4-any)ipd_0.1.2.tgz(r-4.3-any)
ipd_0.1.2.tar.gz(r-4.5-noble)ipd_0.1.2.tar.gz(r-4.4-noble)
ipd_0.1.2.tgz(r-4.4-emscripten)ipd_0.1.2.tgz(r-4.3-emscripten)
ipd.pdf |ipd.html✨
ipd/json (API)

# Install 'ipd' in R:
install.packages('ipd', repos = c('https://ipd-tools.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/ipd-tools/ipd/issues

On CRAN:

5.83 score 4 stars 5 scripts 322 downloads 49 exports 79 dependencies

Last updated 7 days agofrom:cc66fa6df3. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 15 2024
R-4.5-winOKNov 15 2024
R-4.5-linuxOKNov 15 2024
R-4.4-winOKNov 15 2024
R-4.4-macOKNov 15 2024
R-4.3-winOKNov 15 2024
R-4.3-macOKNov 15 2024

Exports:Aaugmentcalc_lhat_glmcompute_cdfcompute_cdf_diffest_iniglanceipdlink_gradlink_Hessianlog1pexplogistic_get_statsmean_psimean_psi_popolsols_get_statsoptim_estoptim_weightspostpi_analytic_olspostpi_boot_logisticpostpi_boot_olsppi_logisticppi_meanppi_olsppi_plusplus_logisticppi_plusplus_logistic_estppi_plusplus_meanppi_plusplus_mean_estppi_plusplus_olsppi_plusplus_ols_estppi_plusplus_quantileppi_plusplus_quantile_estppi_quantilepsipspa_logisticpspa_meanpspa_olspspa_poissonpspa_quantilepspa_yrectified_cdfrectified_p_valueSigma_calsim_data_ysimdattidywlszconfint_genericzstat_generic

Dependencies:caretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverforeachfuturefuture.applygamgenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6randomForestrangerRColorBrewerRcppRcppEigenrecipesreshape2rlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr

Getting Started with the ipd Package

Rendered fromipd.Rmdusingknitr::rmarkdownon Nov 15 2024.

Last update: 2024-11-15
Started: 2024-07-09

Readme and manuals

Help Manual

Help pageTopics
Calculation of the matrix A based on single datasetA
Augment Data from an IPD Fitaugment.ipd
Estimate PPI++ Power Tuning Parametercalc_lhat_glm
Empirical CDF of the Datacompute_cdf
Empirical CDF Differencecompute_cdf_diff
Initial estimationest_ini
Glance at an IPD Fitglance.ipd
Inference on Predicted Data (ipd)ipd
Gradient of the link functionlink_grad
Hessians of the link functionlink_Hessian
Log1p Exponentiallog1pexp
Logistic Regression Gradient and Hessianlogistic_get_stats
Sample expectation of psimean_psi
Sample expectation of PSPA psimean_psi_pop
Ordinary Least Squaresols
OLS Gradient and Hessianols_get_stats
One-step update for obtaining estimatoroptim_est
One-step update for obtaining the weight vectoroptim_weights
PostPI OLS (Analytic Correction)postpi_analytic_ols
PostPI Logistic Regression (Bootstrap Correction)postpi_boot_logistic
PostPI OLS (Bootstrap Correction)postpi_boot_ols
PPI Logistic Regressionppi_logistic
PPI Mean Estimationppi_mean
PPI OLSppi_ols
PPI++ Logistic Regressionppi_plusplus_logistic
PPI++ Logistic Regression (Point Estimate)ppi_plusplus_logistic_est
PPI++ Mean Estimationppi_plusplus_mean
PPI++ Mean Estimation (Point Estimate)ppi_plusplus_mean_est
PPI++ OLSppi_plusplus_ols
PPI++ OLS (Point Estimate)ppi_plusplus_ols_est
PPI++ Quantile Estimationppi_plusplus_quantile
PPI++ Quantile Estimation (Point Estimate)ppi_plusplus_quantile_est
PPI Quantile Estimationppi_quantile
Print IPD Fitprint.ipd
Print Summary of IPD Fitprint.summary.ipd
Estimating equationpsi
PSPA Logistic Regressionpspa_logistic
PSPA Mean Estimationpspa_mean
PSPA OLS Estimationpspa_ols
PSPA Poisson Regressionpspa_poisson
PSPA Quantile Estimationpspa_quantile
PSPA M-Estimation for ML-predicted labelspspa_y
Rectified CDFrectified_cdf
Rectified P-Valuerectified_p_value
Variance-covariance matrix of the estimation equationSigma_cal
Simulate the data for testing the functionssim_data_y
Data generation function for various underlying modelssimdat
Summarize IPD Fitsummary.ipd
Tidy an IPD Fittidy.ipd
Weighted Least Squareswls
Normal Confidence Intervalszconfint_generic
Compute Z-Statistic and P-Valuezstat_generic