Introduction | Import Sample data. | Prepare Inputs | Estimate ATE with SMMAL & Output | Using Your Own custom_model_fun | Understanding SMMAL_ada_lasso | Input of SMMAL_ada_lasso | Demonstration of how to run SMMAL_ada_lasso & Output of SMMAL_ada_lasso
Package: SMMAL 0.0.5
SMMAL: Semi-Supervised Estimation of Average Treatment Effects
Provides a pipeline for estimating the average treatment effect via semi-supervised learning. Outcome regression is fit with cross-fitting using various machine learning method or user customized function. Doubly robust ATE estimation leverages both labeled and unlabeled data under a semi-supervised missing-data framework. For more details see Hou et al. (2021) <doi:10.48550/arxiv.2110.12336>. A detailed vignette is included.
Authors:
SMMAL_0.0.5.tar.gz
SMMAL_0.0.5.zip(r-4.7-any)SMMAL_0.0.5.zip(r-4.6-any)SMMAL_0.0.5.zip(r-4.5-any)
SMMAL_0.0.5.tgz(r-4.6-any)SMMAL_0.0.5.tgz(r-4.5-any)
SMMAL_0.0.5.tar.gz(r-4.7-any)SMMAL_0.0.5.tar.gz(r-4.6-any)
SMMAL_0.0.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
SMMAL/json (API)
| # Install 'SMMAL' in R: |
| install.packages('SMMAL', repos = c('https://hjmarquis.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:d373574b0a. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 338 | ||
| source / vignettes | OK | 257 | ||
| linux-release-x86_64 | OK | 268 | ||
| macos-release-arm64 | OK | 331 | ||
| macos-oldrel-arm64 | OK | 315 | ||
| windows-devel | OK | 304 | ||
| windows-release | OK | 296 | ||
| windows-oldrel | OK | 299 | ||
| wasm-release | OK | 138 |
Exports:ate.SSLcfcompute_parametercross_validationparam_funSMMALSMMAL_ada_lasso
Dependencies:codetoolsdata.tableforeachglmnetiteratorsjsonlitelatticeMatrixrandomForestRcppRcppArmadilloRcppEigenshapesplines2survivalxgboost
