Package: POPInf 1.0.0
Jiacheng Miao
POPInf: Assumption-Lean and Data-Adaptive Post-Prediction Inference
Implementation of assumption-lean and data-adaptive post-prediction inference (POPInf), for valid and efficient statistical inference based on data predicted by machine learning. See Miao, Miao, Wu, Zhao, and Lu (2023) <arxiv:2311.14220>.
Authors:
POPInf_1.0.0.tar.gz
POPInf_1.0.0.zip(r-4.5)POPInf_1.0.0.zip(r-4.4)POPInf_1.0.0.zip(r-4.3)
POPInf_1.0.0.tgz(r-4.4-any)POPInf_1.0.0.tgz(r-4.3-any)
POPInf_1.0.0.tar.gz(r-4.5-noble)POPInf_1.0.0.tar.gz(r-4.4-noble)
POPInf_1.0.0.tgz(r-4.4-emscripten)POPInf_1.0.0.tgz(r-4.3-emscripten)
POPInf.pdf |POPInf.html✨
POPInf/json (API)
| # Install 'POPInf' in R: |
| install.packages('POPInf', repos = c('https://qlu-lab.r-universe.dev', 'https://cloud.r-project.org')) |
The latest version of this package failed to build. Look at thebuild logs for more information.
Bug tracker:https://github.com/qlu-lab/popinf/issues
Last updated 9 months agofrom:6177791fef. Checks:OK: 1 NOTE: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Aug 19 2024 |
| R-4.5-win | NOTE | Aug 19 2024 |
| R-4.5-linux | NOTE | Aug 19 2024 |
| R-4.4-win | NOTE | Aug 19 2024 |
| R-4.4-mac | NOTE | Aug 19 2024 |
| R-4.3-win | NOTE | Aug 19 2024 |
| R-4.3-mac | NOTE | Aug 19 2024 |
Exports:Aest_inilink_gradlink_Hessianmean_psimean_psi_popoptim_estoptim_weightspop_MpsiSigma_calsim_data
Dependencies:MASSrandomForest
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Calculation of the matrix A based on single dataset | A |
| Initial estimation | est_ini |
| gradient of the link function | link_grad |
| Hessians of the link function | link_Hessian |
| Sample expectation of psi | mean_psi |
| Sample expectation of POP-Inf psi | mean_psi_pop |
| Gradient descent for obtaining estimator | optim_est |
| Gradient descent for obtaining the weight vector | optim_weights |
| POP-Inf M-Estimation | pop_M |
| Esimating equation | psi |
| Variance-covariance matrix of the estimation equation | Sigma_cal |
| Simulate the data for testing the functions | sim_data |